All things {AI} in simple terms

A/B Testing

A/B testing is a method of comparing two versions of something to determine which performs better. Users are randomly shown version A or B, and statistical analysis determines which version achieves the desired outcome.

Abstraction Hierarchy

An abstraction hierarchy organizes complex systems by levels of detail. Higher levels offer simplified views, hiding implementation. Lower levels expose intricate details. This layered approach manages complexity and promotes modularity.

Action Execution

Action Execution is the process of carrying out a specific task or operation within a software system, triggered by an event or condition. It involves invoking the necessary code, processing data, and producing a result or side effect.

Adaptive Computation

Adaptive Computation is a computational approach where systems modify their behavior based on incoming data or environmental changes, optimizing performance or learning new patterns dynamically.

Adversarial Testing

Adversarial testing is a method to evaluate a system by intentionally providing inputs designed to cause failure, expose vulnerabilities, or reveal unexpected behavior. It helps improve robustness and security.

Agent Swarms

Agent Swarms are a group of AI agents that work together to solve complex problems. These agents communicate, coordinate, and collaborate, leveraging diverse skills and knowledge to achieve a common goal, often exceeding the capabilities of individual agents.

AGI Milestones

AGI Milestones are specific, measurable achievements demonstrating progress toward Artificial General Intelligence (AGI). These milestones track capabilities like reasoning, problem-solving, learning, and adaptability, indicating advancements in AI's human-level intelligence.

AGI Safety

AGI Safety is research dedicated to ensuring that advanced artificial general intelligence (AGI) systems are aligned with human values and goals, preventing unintended harmful consequences from their actions.

AI Agents

An AI Agent is an autonomous entity that perceives its environment through sensors and acts upon that environment through actuators to achieve specific goals. These agents can be simple or complex, operating in real or simulated environments.

AI Alignment

AI Alignment is ensuring AI systems pursue intended goals. It addresses the challenge of creating AI that is beneficial, safe, and reliable by aligning its objectives with human values and intentions.

AI as a Service (AIaaS)

AI as a Service (AIaaS) is a cloud computing offering that provides access to AI tools, algorithms, and infrastructure, enabling businesses to leverage AI capabilities without significant upfront investment or expertise.

AI Benchmarks

AI Benchmarks are standardized tests used to evaluate the performance of artificial intelligence systems. They measure speed, accuracy, and efficiency across specific tasks, providing a basis for comparison and improvement.

AI Bias

AI bias is systematic and repeatable errors in AI outputs due to flawed assumptions in the algorithm or training data. This results in unfair or discriminatory outcomes, impacting specific groups.

AI Governance

AI Governance is the framework of policies, procedures, and practices designed to manage the development and deployment of AI systems responsibly, ethically, and in compliance with regulations. It ensures accountability, transparency, and fairness.

AI Test Generation

AI Test Generation is the use of artificial intelligence to automatically create test cases, test data, and test scripts for software applications, improving efficiency and coverage.

AI Training Data

AI training data is the labeled or unlabeled data used to train machine learning models. It enables algorithms to learn patterns, make predictions, and improve performance through iterative exposure and adjustment.

AI-Driven Test Maintenance

AI-Driven Test Maintenance is the use of artificial intelligence (AI) and machine learning (ML) techniques to automate and improve the process of updating and maintaining automated software tests, reducing manual effort and improving test reliability.

Analogical Reasoning

Analogical reasoning is a cognitive process of transferring knowledge from one subject (the source) to another (the target) based on perceived similarities. It involves identifying shared relationships, patterns, or structures between the two.

Anthropomorphism

Anthropomorphism is attributing human traits, emotions, or intentions to non-human entities, like animals, objects, or AI systems. This can lead to misinterpretations of their behavior and capabilities.

API Testing AI

API Testing AI is the use of artificial intelligence and machine learning to automate and enhance API testing processes, improving efficiency, accuracy, and coverage. It leverages AI to generate test cases, predict failures, and analyze test results.

Approximate Nearest Neighbor (ANN)

Approximate Nearest Neighbor (ANN) search finds data points closest to a query point, accepting slight inaccuracies for significantly faster search speeds than exact methods. Useful for large datasets where speed is crucial.

Artificial Consciousness

Artificial Consciousness is a hypothetical AI with subjective awareness, self-awareness, and qualia, mirroring human consciousness. It involves creating machines that not only process information but also experience it.

Artificial General Intelligence (AGI)

Artificial General Intelligence is a hypothetical AI with human-level cognitive abilities: understanding, learning, adapting, and implementing knowledge across a wide range of tasks. It can perform any intellectual task that a human being can.

Artificial Neural Networks

Artificial neural networks are computational models inspired by the structure and function of biological neural networks. They consist of interconnected nodes (neurons) organized in layers that process information to learn complex patterns from data.

Artificial Super Intelligence (ASI)

Artificial Super Intelligence is a hypothetical AI exceeding human intelligence and capabilities across all domains, including creativity, problem-solving, and general wisdom. It surpasses human limits.

Automated Metrics

Automated metrics are performance indicators collected and analyzed automatically by software systems, providing real-time insights into application health, usage, and business impact without manual intervention.

Automatic Evaluation

Automatic Evaluation is the process of using algorithms and metrics to assess the performance of a system, model, or piece of code without human intervention. This allows for rapid, objective, and scalable performance analysis.

AutoML

Automated Machine Learning is the automation of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. It includes tasks like data preprocessing, feature engineering, model selection, and hyperparameter opt

Autonomous Testing Agents

Autonomous Testing Agents are AI-powered systems that independently design, execute, and analyze software tests, adapting to changing code and environments without constant human intervention, improving efficiency and coverage.

Autorater Evaluation

Autorater Evaluation is an automated process for assessing the quality of machine learning model outputs, particularly in areas like natural language processing, by comparing them to pre-defined benchmarks or human-generated 'gold standard' data.

AutoTokenizer

An AutoTokenizer automatically selects the appropriate tokenizer for a given pre-trained model. It simplifies the process by handling the complexities of different tokenization methods, ensuring compatibility and optimal performance.

Causal Reasoning

Causal reasoning is the process of identifying cause-and-effect relationships. It involves understanding how one event or action influences another, allowing for prediction and intervention. It's crucial for decision-making and problem-solving.

Chain of Thought (CoT)

Chain of Thought is a prompting technique for large language models (LLMs) that encourages them to explain their reasoning process step-by-step before arriving at a final answer. This improves accuracy in complex reasoning tasks.

Chain Prompting

Chain prompting is a technique used in large language models where the output of one prompt is fed as input to a subsequent prompt, creating a chain of prompts to achieve a more complex task.

Chaos Testing AI

Chaos Testing AI uses artificial intelligence to automate and optimize chaos engineering. It intelligently injects failures into systems, analyzes the results, and learns to predict vulnerabilities, improving system resilience.

Chroma

Chroma is the color information in an image, independent of brightness. It represents the hue and saturation, determining the color's type and intensity, while luminance handles the image's lightness.

Claude (Anthropic)

Claude (Anthropic) is a family of large language models created by Anthropic, designed for helpful, harmless, and honest AI interactions. It excels at tasks like summarization, question answering, and content creation, prioritizing safety and ethical considerations.

Code Language Models

Code Language Models are AI models trained on code to understand, generate, and manipulate code in various programming languages. They assist in code completion, bug detection, and code translation.

Code Review Culture

Code Review Culture refers to the collective attitudes, practices, and norms that shape how teams approach and conduct code reviews, emphasizing collaboration, learning, and code quality.

Cognitive Architecture

A cognitive architecture is a framework for creating intelligent agents, providing fixed structural and processing constraints. It specifies the underlying infrastructure for cognition, including memory, attention, and decision-making.

Cognitive Architecture for AGI

A Cognitive Architecture for AGI is a framework defining the structure and processes of an artificial general intelligence (AGI) system. It specifies how knowledge is represented, processed, and utilized to achieve human-level cognitive abilities across diverse tasks.

Cognitive Testing Automation

Cognitive Testing Automation is the use of AI and machine learning to automate the process of testing software for cognitive abilities like reasoning, learning, and problem-solving, ensuring applications function as expected in complex scenarios.

Collaborative ML Development

Collaborative ML Development is a software engineering approach where teams jointly build, test, and deploy machine learning models. It emphasizes shared code, data, and infrastructure, promoting efficiency and reproducibility through collaborative workflows and tools.

Comparative Analysis

Comparative analysis is a method of evaluating different options by identifying and assessing their strengths and weaknesses relative to each other, often to inform decision-making.

Completions

A Completion is the output generated by a language model in response to a given prompt. It represents the model's attempt to continue or complete the provided text, based on its training data and learned patterns.

Computer Vision Agents

Computer Vision Agents are AI systems that perceive and interpret visual information from images or videos to perform tasks like object detection, image classification, and scene understanding, enabling automated decision-making.

Constitutional AI

Constitutional AI is a technique to train AI models using a set of principles (a constitution) rather than relying solely on human-labeled data, promoting safety and alignment with desired values.

Constitutional AI Training

Constitutional AI training is a technique for aligning large language models (LLMs) with human values by training them to adhere to a 'constitution' of principles, guiding their responses without direct human feedback on every interaction.

Constrained Prompting

Constrained prompting limits LLM outputs by defining specific formats, keywords, or structures. It ensures responses align with predefined rules, enhancing control and predictability in AI-generated content.

Context Awareness

Context awareness is a system's ability to gather and interpret information about its environment and adapt its behavior accordingly. This includes location, time, user activity, and device status.

Context Window

The context window is the amount of text a language model can consider when processing or generating text. It determines the scope of information the model uses to understand context and make predictions. A larger window allows for better understanding of longer texts.

Context Window Management

Context window management is the process of optimizing the use of a language model's limited context window to improve performance, reduce computational costs, and handle longer input sequences effectively.

Contrastive Learning

Contrastive learning is a self-supervised learning technique where similar data points are pulled closer in embedding space, while dissimilar ones are pushed further apart, learning robust representations without labeled data.

Convergent Thinking

Convergent thinking is a cognitive process focusing on finding one well-established solution to a problem. It emphasizes logic, accuracy, and speed, often used in structured situations with clear goals.

Cost Attribution

Cost attribution is the process of identifying and assigning costs to specific activities, products, services, or projects within an organization. It helps understand the true cost of each item and improve decision-making.

Cost Per Token

Cost Per Token is the price paid for processing each token (a unit of text) by a language model. It's a key metric for budgeting and optimizing AI application costs. Lower cost per token allows for more extensive processing at a given budget.

Counterfactual Thinking

Counterfactual thinking involves mentally simulating alternative past events or scenarios to understand what might have happened if circumstances were different. It's a 'what if' thought process.

Cross-Browser Testing AI

Cross-Browser Testing AI automates testing websites/apps across different browsers (Chrome, Firefox, Safari, etc.) using AI. It identifies UI inconsistencies, functional bugs, and responsiveness issues, improving test coverage and efficiency.

Cross-Modal Generation

Cross-Modal Generation is the process of creating content in one modality (e.g., image, audio, text) from input data in a different modality. It leverages AI models to translate information across different data types, enabling applications like image captioning or text-to-speech.

DALL-E (OpenAI)

DALL-E is an OpenAI model that generates digital images from natural language descriptions. It leverages deep learning to interpret text prompts and create corresponding visuals, enabling users to produce diverse and imaginative artwork.

Data Augmentation

Data augmentation is a technique to artificially increase the size of a training dataset by creating modified versions of existing data. This helps improve the generalization ability and robustness of machine learning models.

Data Bias in AI

Data bias in AI is systematic error in training data that skews AI model outcomes, leading to unfair or inaccurate predictions, disproportionately affecting specific groups. It arises from skewed sampling, flawed data collection, or reflecting existing societal biases.

Datasets Hub

A Datasets Hub is a centralized repository for datasets, often associated with machine learning. It simplifies dataset discovery, sharing, and collaboration, providing tools for versioning, exploration, and integration into ML workflows.

Deepfake

A deepfake is synthetic media manipulated using deep learning to replace one person's likeness with another's, often used to create realistic-looking but fabricated videos or images.

DeepSeek

DeepSeek is a suite of AI models developed by DeepSeek AI, encompassing large language models (LLMs) and other AI tools designed for various applications, including code generation, text understanding, and creative content creation.

Desktop Automation

Desktop Automation is the process of using software to automate repetitive tasks performed on a desktop computer, improving efficiency and reducing errors. It mimics user actions through software.

Developer Experience (DX)

Developer Experience (DX) refers to the overall experience and satisfaction of developers while working with tools, processes, and environments in software development.

Diffusion Models

Diffusion models are generative models that learn to create data by gradually adding noise to training data and then learning to reverse this process to generate new samples.

Divergent Thinking

Divergent thinking is a thought process used to generate creative ideas by exploring many possible solutions. It involves breaking down a problem into different aspects to create multiple ideas.

Document Chunking

Document chunking is the process of dividing a large document into smaller, more manageable segments. These segments, or chunks, are designed to retain contextual meaning and facilitate efficient processing, especially in information retrieval and NLP tasks.

Factuality

Factuality is the degree to which a statement, claim, or piece of information aligns with objective reality and can be verified with evidence. It reflects the accuracy and truthfulness of the information presented.

FAISS (Facebook AI Similarity Search)

FAISS is a library for efficient similarity search and clustering of high-dimensional vectors. It enables fast retrieval of nearest neighbors in large datasets, crucial for applications like recommendation systems and image retrieval.

Federated Fine-tuning

Federated fine-tuning is a machine learning technique that adapts a pre-trained model to specific client datasets without directly accessing or centralizing the data. It leverages federated learning principles to maintain data privacy and security during the fine-tuning process.

Federated Learning

Federated learning is a distributed machine learning approach that trains a model across multiple decentralized devices or servers holding local data samples without exchanging them. This preserves data privacy and reduces communication costs.

Few-Shot Chain of Thought

Few-shot Chain-of-Thought is a prompting technique for large language models (LLMs) that provides a few examples demonstrating step-by-step reasoning to guide the model in solving complex problems. This improves accuracy compared to standard few-shot prompting.

Few-Shot Learning

Few-shot learning is a machine learning approach enabling models to generalize from limited data. It contrasts with traditional methods needing extensive training datasets, leveraging prior knowledge to learn new tasks efficiently with only a few examples.

Few-Shot Prompting

Few-shot prompting is a technique for training language models. It involves providing a few examples of the desired input-output behavior in the prompt itself, guiding the model to generate similar outputs for new, unseen inputs.

Few-Shot Video Generation

Few-shot video generation creates new videos from only a handful of example videos. It leverages machine learning to understand video content and style, enabling the generation of novel videos with similar characteristics, even with limited training data.

Fine-tuning

Fine-tuning is the process of taking a pre-trained model and further training it on a new, smaller dataset to adapt it for a specific task. This leverages existing knowledge, improving performance and reducing training time compared to training from scratch.

Flash Attention

Flash Attention is an efficient attention mechanism that reduces memory access and accelerates training for transformers by processing attention in blocks and using kernel fusion.

Flow State in Programming

Flow State in Programming refers to the optimal mental state where developers experience deep focus, heightened creativity, and peak productivity while coding.

Foundation Models

Foundation models are large AI models trained on broad data, adaptable to various downstream tasks. They exhibit emergent properties and serve as a base for specialized applications through fine-tuning or prompting.

Gemini (Google)

Gemini (Google) is a multimodal AI model developed by Google. It's designed to process and generate text, images, audio, video, and code, excelling in complex reasoning and understanding different types of information seamlessly.

Generalization

Generalization is the ability of a model to accurately predict outcomes on new, unseen data after being trained on a specific dataset. It reflects how well the model adapts its learned knowledge to different scenarios.

Generative Adversarial Networks

Generative Adversarial Networks are a machine learning framework where two neural networks (a generator and a discriminator) compete. The generator creates new data instances, while the discriminator evaluates them for authenticity.

GLUE Benchmark

The GLUE Benchmark is a set of diverse natural language understanding tasks used to evaluate the performance of machine learning models. It assesses a model's ability to generalize across different text understanding challenges.

GPT (OpenAI)

GPT (Generative Pre-trained Transformer) is a type of large language model (LLM) created by OpenAI. It uses deep learning to generate human-like text, translate languages, and answer questions. GPT models are pre-trained on massive datasets and fine-tuned for specific tasks.

Graph Neural Networks

Graph Neural Networks are neural networks that operate on graph structures, enabling analysis and prediction based on relationships between nodes. They excel in tasks where data is inherently relational.

Graph RAG

Graph RAG enhances retrieval-augmented generation by using graph databases to represent and reason over knowledge, improving context retrieval for LLMs. It enables more accurate and relevant responses by leveraging relationships between data points.

Hallucination

Hallucination in AI is when a model generates outputs that are nonsensical, factually incorrect, or not grounded in its training data. It confidently presents false or misleading information as if it were true.

Hierarchical Task Decomposition

Hierarchical Task Decomposition is a method of breaking down a complex task into smaller, more manageable subtasks, arranged in a hierarchical structure. This simplifies planning, execution, and problem-solving.

Hugging Face Hub

The Hugging Face Hub is a platform for hosting and sharing machine learning models, datasets, and applications. It provides version control, collaboration tools, and community features, enabling developers to easily access and contribute to the open-source ML ecosystem.

Human Evaluation

Human evaluation is the process of assessing system performance using human judgment. It measures the quality of outputs based on subjective criteria like relevance, accuracy, and user satisfaction.

Human-AI Collaboration

Human-AI Collaboration is a synergistic partnership where humans and AI systems combine their strengths to achieve outcomes neither could accomplish as effectively alone. It leverages human creativity, critical thinking, and contextual awareness with AI's speed, data processing, and pattern recognit

Hybrid Search

Hybrid search combines multiple search techniques to improve search result relevance and accuracy. It often blends semantic and keyword-based methods, leveraging the strengths of each to overcome individual limitations and provide more comprehensive results.

Hyperparameter Sweep

A hyperparameter sweep is a systematic search for the optimal combination of hyperparameters for a machine learning model. It involves training and evaluating the model with different hyperparameter sets to identify the configuration that yields the best performance.

Hyperparameter Tuning

Hyperparameter tuning optimizes a machine learning model's performance by finding the best set of hyperparameters. These parameters are set before training and control the learning process itself. It's crucial for achieving optimal model accuracy and generalization.

Machine Learning Training

Machine learning training is the process of teaching a model to learn patterns from data. It involves feeding the model with labeled or unlabeled data, adjusting its parameters, and evaluating its performance until it achieves a desired level of accuracy.

Meta-Prompting

Meta-Prompting uses a large language model (LLM) to generate or refine prompts for other LLMs. It automates prompt engineering, improving efficiency and effectiveness in eliciting desired outputs from AI models.

Metacognition in AI

Metacognition in AI is the ability of an AI system to 'think about thinking'. It involves self-awareness, monitoring its own processes, evaluating its performance, and adapting its strategies for improved problem-solving and learning.

Metamorphic Testing AI

Metamorphic Testing AI uses AI to automate the creation of metamorphic relations and test cases for software testing. It learns system behavior to generate diverse tests, improving test coverage and defect detection without needing explicit specifications.

Midjourney

Midjourney is an independent research lab creating an AI program that generates images from textual descriptions, similar to DALL-E and Stable Diffusion. It's accessible via a Discord server using text commands.

Milvus

Milvus is an open-source vector database designed for scalable similarity search and analytics. It efficiently stores, indexes, and manages massive embedding vectors generated by deep learning models and other AI applications, enabling fast retrieval of similar vectors.

Mistral

Mistral is a family of large language models (LLMs) developed by Mistral AI, known for their efficiency and performance. They are designed to be adaptable and customizable, offering strong capabilities in text generation, understanding, and code generation.

Mixture of Experts (MoE)

A Mixture of Experts (MoE) is a machine learning model composed of multiple 'expert' sub-networks and a 'gate' network. The gate dynamically selects which experts to use for a given input, enabling specialization and increased model capacity.

Model Artifacts

Model artifacts are the tangible outputs produced during the lifecycle of a machine learning model, including the trained model, metadata, and associated files necessary for deployment and reproducibility.

Model Bias

Model bias is systematic error in a model's predictions due to flawed assumptions in the learning algorithm, training data, or feature engineering. It leads to unfair or inaccurate outcomes for certain groups.

Model Cards

Model Cards are documentation artifacts providing information about a machine learning model, including its intended use, performance metrics, training data, and potential biases.

Model Compression

Model compression reduces the size of machine learning models, making them faster, more energy-efficient, and deployable on resource-constrained devices like mobile phones or embedded systems. It maintains acceptable accuracy while minimizing computational cost.

Model Deployment

Model deployment is the process of integrating a trained machine learning model into an existing production environment to make predictions on new data. It involves making the model accessible for use by applications and users.

Model Distillation

Model distillation is a technique to compress a large, complex model (teacher) into a smaller, more efficient model (student) while preserving its performance. The student learns from the teacher's soft probabilities instead of just hard labels.

Model Evaluation Pipeline

A Model Evaluation Pipeline is an automated process for assessing the performance of machine learning models. It encompasses data preparation, model scoring, and metric calculation to provide insights into model quality and identify areas for improvement.

Model Hub Integration

Model Hub Integration is the process of connecting software applications to centralized repositories of pre-trained machine learning models. This allows developers to easily access, deploy, and manage models without needing to train them from scratch.

Model Monitoring

Model monitoring is the process of tracking a machine learning model's performance in a production environment to ensure accuracy, reliability, and prevent degradation over time. It involves collecting and analyzing data to identify issues and trigger alerts.

Model Parameters

Model parameters are internal variables learned by a model during training that define its skill on a problem, such as weights and biases in neural networks.

Model Performance Monitoring

Model Performance Monitoring is the ongoing process of tracking and analyzing the performance of machine learning models after deployment to ensure accuracy, reliability, and relevance over time.

Model Pruning

Model pruning reduces the size and complexity of a machine learning model by removing unimportant connections or parameters. This results in a smaller, faster, and more efficient model with minimal impact on accuracy.

Model Quantization

Model quantization is a technique that reduces the precision of a neural network's weights and activations, typically from 32-bit floating point to lower bit representations like 8-bit integer, to decrease model size and accelerate inference.

Model Repositories

A model repository is a centralized storage system for machine learning models, their metadata, and related artifacts. It enables versioning, access control, and collaboration in the model development lifecycle.

Model Serving

Model serving is the process of deploying a trained machine learning model into a production environment where it can be used to make predictions on new data. It involves making the model accessible through an API or other interface.

Model Training Process

The model training process is the iterative procedure of teaching a machine learning model to make accurate predictions by feeding it data, adjusting its parameters based on performance, and validating its effectiveness.

Model Versioning

Model versioning is the practice of tracking and managing changes to machine learning models throughout their lifecycle. It ensures reproducibility, allows for comparison of different model iterations, and facilitates rollback to previous versions if needed.

Model Weights

The model weights are parameters within a neural network that determine the strength of connections between neurons. These values are learned during training and represent the knowledge the model has acquired from the data. They are crucial for making accurate predictions.

Multi-Application Coordination

Multi-Application Coordination is the process of managing interactions and dependencies between multiple independent software applications to achieve a unified goal or maintain system-wide consistency. It ensures seamless operation across diverse systems.

Multi-Hop Reasoning

Multi-hop reasoning involves inferring information by connecting multiple pieces of evidence or facts, requiring several logical steps to reach a conclusion that isn't explicitly stated. It's crucial for complex problem-solving.

Multi-Modal Learning

Multi-Modal Learning is a machine learning approach that trains models to process and relate information from multiple data modalities, such as text, images, and audio, to gain a more comprehensive understanding.

Multi-Modal RAG

Multi-Modal RAG enhances standard Retrieval-Augmented Generation by incorporating diverse data types beyond text, such as images, audio, and video. This allows LLMs to generate more comprehensive and contextually relevant responses by leveraging richer information sources.

Multimodal Chain-of-Thought

Multimodal Chain-of-Thought extends Chain-of-Thought prompting to handle diverse data types (text, images, audio). It enables models to reason step-by-step, integrating information from multiple modalities to arrive at a final answer or decision.

Multimodal Few-Shot Learning

Multimodal Few-Shot Learning is a machine learning technique enabling models to generalize from limited examples across different data types (e.g., text, images, audio). It leverages knowledge from related tasks and modalities to quickly adapt to new scenarios with minimal training data.

Multimodal Models

Multimodal models are AI systems that process and integrate information from multiple data modalities, such as text, images, audio, and video, to perform tasks that require understanding across different types of data.

Multimodal Prompting

Multimodal prompting uses various data types (text, images, audio, video) as input to guide AI models. It allows for richer, more nuanced interactions, enabling models to understand and respond to complex, real-world scenarios beyond text alone.

Pair Programming

Pair Programming is a software development technique where two developers work together at one workstation, with one actively coding (driver) and the other reviewing and guiding (navigator).

PaLM (Google)

PaLM is a large language model from Google AI. It excels at complex reasoning, code generation, and multilingual tasks. PaLM uses a transformer-based architecture and has been trained on a massive dataset of text and code.

Pattern Recognition

Pattern recognition is the automated identification of regularities in data. It uses algorithms to classify data into categories based on learned patterns.

Performance Metrics

Performance Metrics are quantifiable measurements used to evaluate the efficiency, effectiveness, and overall performance of a system, application, or process. They provide insights into resource utilization, speed, and stability.

Performance Testing AI

Performance Testing AI is the use of artificial intelligence and machine learning techniques to automate, optimize, and enhance software performance testing, improving efficiency and accuracy.

pgvector

pgvector is an open-source PostgreSQL extension for storing and querying vector embeddings. It allows efficient similarity searches for AI applications by enabling storage and comparison of high-dimensional vectors directly within the database.

Phi Architecture

The Phi architecture is a transformer-based model known for achieving high performance with relatively small size, emphasizing efficient training and inference. It leverages innovative techniques to reduce computational demands while maintaining accuracy.

Pinecone

Pinecone is a fully managed, cloud-native vector database designed for large-scale AI applications. It provides efficient similarity search and retrieval, enabling fast and accurate results for tasks like recommendation systems and semantic search.

Pipeline API

A Pipeline API is an interface that allows developers to define and execute a series of data processing steps (a pipeline) in a structured and automated manner. It simplifies complex workflows by chaining operations together.

Prompt Chaining

Prompt chaining involves connecting the output of one large language model (LLM) prompt as the input for another, creating a sequence of prompts to achieve a complex task. This allows for breaking down problems into smaller, manageable steps.

Prompt Engineering

Prompt engineering is the art of crafting effective prompts to elicit desired responses from large language models. It involves designing inputs that guide the model towards generating accurate, relevant, and coherent outputs.

Prompt Optimization

Prompt optimization is the process of refining input text given to AI models to elicit desired outputs. It involves crafting prompts that are clear, specific, and effective in guiding the model to generate accurate and relevant responses.

Prompt Templates

Prompt templates are pre-defined structures for crafting inputs to large language models (LLMs), guiding the LLM to generate specific and consistent outputs. They often include variables and instructions to tailor the prompt for different use cases.

Proprietary Models

Proprietary models are AI or machine learning models whose architecture, training data, and weights are kept secret and under the exclusive control of the developing organization. Access is typically granted through APIs or licensed software, restricting modification or redistribution.

RAG Evaluation Metrics

RAG Evaluation Metrics are quantitative measures used to assess the performance of Retrieval-Augmented Generation (RAG) systems, evaluating the quality of both the retrieved context and the generated response. They help optimize RAG pipelines for accuracy, relevance, and coherence.

RAG Pipeline

A RAG Pipeline enhances LLMs by retrieving information from external sources to ground the model's responses in factual data, reducing hallucinations and improving accuracy. It involves indexing, retrieval, and generation stages.

Rate Limiting

Rate limiting controls the number of requests a user or service can make to an API or resource within a specific timeframe. It prevents abuse, ensures fair usage, and maintains service availability and performance.

ReAct Framework

ReAct is a framework for LLMs enabling them to reason (generate verbal reasoning traces) and act (execute actions, like using tools). It combines reasoning and acting, allowing for more complex problem-solving and interaction with environments.

Recursive Retrieval

Recursive retrieval is a search technique where results are used to refine the subsequent search, iteratively narrowing down to the most relevant information. This process repeats until a satisfactory result or a defined limit is reached.

Recursive Self-Improvement

Recursive self-improvement is a process where an AI system improves its own capabilities, then uses those improved capabilities to further enhance itself, leading to a potentially rapid and escalating cycle of advancement.

Regression Testing AI

Regression Testing AI is the use of artificial intelligence to automate and optimize regression testing, improving efficiency and accuracy in identifying software defects after code changes.

Reinforcement Learning

Reinforcement Learning is a machine learning paradigm where an agent learns to make decisions in an environment to maximize a cumulative reward. It learns through trial and error, receiving feedback in the form of rewards or penalties.

Retrieval Augmented Generation

Retrieval Augmented Generation is a technique that enhances large language models by retrieving information from an external knowledge source and incorporating it into the generated text, improving accuracy and reducing hallucinations.

Robustness

Robustness is the degree to which a computer system or algorithm functions correctly in the face of invalid inputs, stressful environmental conditions, or unexpected usage. It emphasizes stability and reliability under challenging circumstances.

Role Prompting

Role prompting involves instructing a language model to adopt a specific persona or character. This guides the model's responses, shaping its tone, style, and content to align with the assigned role, enhancing relevance and creativity.

Security Testing AI

Security Testing AI is the use of artificial intelligence to automate, enhance, and optimize software security testing processes, identifying vulnerabilities and improving overall security posture.

Self-Consistency

Self-consistency ensures an AI model's outputs are logically coherent and internally consistent. It means the model's responses don't contradict each other, reflecting a stable understanding of the information it processes.

Self-Rewarding Language Models

Self-Rewarding Language Models are AI systems designed to improve their performance autonomously. They generate their own reward signals to guide learning, reducing reliance on external human feedback and enabling continuous self-improvement.

Self-Supervised Learning

Self-Supervised Learning is a machine learning approach where a model learns from unlabeled data by creating its own supervisory signals. It leverages inherent data structure to generate labels for training.

Self-Testing AI

Self-Testing AI is an AI system designed to automatically evaluate its own performance, identify weaknesses, and initiate improvements without external intervention. It uses internal metrics and validation techniques to ensure reliability and accuracy.

Semantic Network

A semantic network is a knowledge representation method using nodes (concepts) and edges (relationships) to depict interconnected meanings. It models relationships between concepts, enabling reasoning and inference.

Semantic Routing

Semantic Routing is a method of directing network traffic based on the meaning and context of the data, rather than just the destination address. It analyzes content to make intelligent forwarding decisions.

Sentiment Analysis

Sentiment analysis is the process of determining the emotional tone behind a body of text. It identifies and categorizes opinions expressed in text, revealing the author's attitude toward a specific topic, product, or service.

Singularity

The technological singularity is a hypothetical point in time when technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization. Often associated with the advent of superintelligence.

Spaces (Hugging Face)

Hugging Face Spaces provides a platform to host and share ML demo apps. It simplifies deployment, allowing users to showcase models through interactive interfaces built with tools like Gradio and Streamlit. It supports static sites and Docker-based applications.

Sparse Mixture of Experts (SMoE)

A Sparse Mixture of Experts (SMoE) is a neural network architecture where only a subset of experts (smaller neural networks) are activated for each input. A gating network determines which experts to use, enabling efficient scaling and specialization.

Speculative Decoding

Speculative Decoding is a technique used to accelerate the inference speed of large language models by predicting multiple possible next tokens in parallel and verifying them against the model's actual output.

Stable Diffusion

Stable Diffusion is a latent text-to-image diffusion model. It generates detailed images conditioned on text descriptions. It operates in a lower-dimensional latent space, improving efficiency and speed compared to pixel-space diffusion models.

Supervised Learning

Supervised learning uses labeled data to train a model to predict outcomes. The model learns a mapping function from input features to output labels, enabling it to classify new, unseen data or predict continuous values.

Swarm Intelligence

Swarm Intelligence is a decentralized, self-organized approach to problem-solving inspired by the collective behavior of social insects like ants and bees. It uses simple agents interacting locally to achieve complex global behavior.

Synapse-Style Relationships

Synapse-Style Relationships are a knowledge representation technique where relationships between entities are explicitly defined and stored as separate objects, similar to how synapses connect neurons in the brain. These relationships have their own properties and can be queried independently.

System 1 and System 2 Thinking

System 1 is fast, intuitive, and emotional thinking. System 2 is slower, more deliberate, and logical thinking. They both influence decision-making.

Task Planning Agents

Task Planning Agents are AI systems designed to autonomously create and execute plans to achieve specific goals. They decompose complex tasks into smaller, manageable steps, considering constraints and available resources to optimize performance.

Temperature

Temperature controls the randomness of predictions in generative models. Higher values increase randomness, leading to more diverse but potentially less accurate outputs. Lower values make outputs more deterministic and predictable.

Test Case Synthesis

Test case synthesis is the automatic generation of test cases from a given specification, model, or code. It aims to create a comprehensive set of tests to ensure software behaves as expected, improving coverage and reducing manual effort.

Test Coverage Analysis AI

Test Coverage Analysis AI uses machine learning to optimize software testing by predicting coverage gaps, suggesting test cases, and prioritizing tests for efficient code coverage.

Test Data Generation AI

Test Data Generation AI uses machine learning to automatically create realistic and varied data for software testing. It learns from existing data patterns to produce synthetic data, improving test coverage and efficiency while protecting sensitive information.

Test Impact Analysis

Test Impact Analysis is the process of identifying the areas of a system that are likely to be affected by a change, helping to prioritize and scope testing efforts effectively.

Test Oracle AI

Test Oracle AI is an AI system that predicts expected outputs for software tests, automating the oracle problem of determining if test results are correct. It learns from data to provide a baseline for comparison, improving testing efficiency and coverage.

Text-to-Image Generation

Text-to-Image Generation is an AI process that uses text descriptions as input to create corresponding images. It leverages machine learning models to translate textual semantics into visual representations.

Thought Verification

Thought Verification is a process used in AI, particularly in reinforcement learning and cognitive architectures, where an agent evaluates the consistency and validity of its own internal reasoning or planned actions before execution, aiming to improve decision-making and reduce errors.

Token Batching

Token batching is a technique that groups multiple independent sequences of tokens into a single batch for processing by a language model, improving throughput and efficiency by maximizing hardware utilization.

Token Caching

Token caching is a technique used to store the results of tokenizing text, avoiding redundant processing. It improves performance by reusing previously computed tokens for identical input strings, reducing latency and computational cost in NLP tasks.

Token Economy

A token economy is a system that uses tokens as a reward for desired behaviors. These tokens can then be exchanged for meaningful rewards or privileges. It's used to incentivize participation and track contributions within a community or system.

Token Optimization

Token Optimization is the process of reducing the number of tokens required to represent a given piece of text or data for processing by a language model, improving efficiency and reducing costs.

Tokens and Tokenization

Tokens are the smallest units of data after text or code is broken down. Tokenization is the process of splitting a larger string of text or code into these smaller units, often for easier processing or analysis.

Toxicity

Toxicity refers to the presence of offensive, harmful, or inappropriate content within a dataset or generated by a model. It includes language that is hateful, disrespectful, or intended to cause harm, impacting user experience and ethical considerations.

Training Dashboard

A Training Dashboard is a visual interface providing insights into the progress and effectiveness of training programs. It tracks key metrics like completion rates, performance scores, and user engagement, enabling data-driven decisions for optimization.

Training Metrics

Training metrics are quantitative measures used to evaluate the performance of a machine learning model during the training process. They provide insights into how well the model is learning from the training data and help identify areas for improvement.

Transformers

A Transformer is a neural network architecture that relies on self-attention mechanisms to weigh the importance of different parts of the input data. It's particularly effective for sequence-to-sequence tasks like translation and text generation, and forms the basis for many large language models.

Transformers Library

The Transformers library provides pre-trained models and tools for natural language processing (NLP). It simplifies using transformer-based architectures like BERT, GPT, and T5 for tasks such as text classification, translation, and generation.

Tree of Thoughts

Tree of Thoughts is a problem-solving framework for LLMs. It extends chain-of-thought prompting by enabling exploration of multiple reasoning paths. The model generates a tree of thoughts, evaluates them, and backtracks to make better decisions.

Value Learning

Value Learning is a type of machine learning where an agent learns to estimate the optimal value of being in a particular state or taking a specific action in a given environment, guiding decision-making.

Variational Autoencoders

A Variational Autoencoder is a type of neural network used for generative modeling. It learns a latent representation of input data, allowing it to generate new data points similar to the training data. VAEs are probabilistic and produce a distribution over the latent space.

Vector Databases

Vector databases are purpose-built to store, manage, and search vector embeddings. These embeddings represent data items as points in a high-dimensional space, capturing semantic relationships for similarity searches and other AI applications.

Vector Embeddings

Vector embeddings are numerical representations of data (text, images, etc.) in a multi-dimensional space. These vectors capture semantic relationships, allowing for similarity comparisons and machine learning tasks.

Vibe Coding

Vibe Coding is a modern approach to software development that emphasizes creating an optimal environment and mindset for developers to achieve peak productivity and creativity.

Vision Language Models (VLMs)

Vision Language Models are AI models that process and understand both images and text. They bridge computer vision and natural language processing, enabling tasks like image captioning, visual question answering, and multimodal reasoning.

Visual Testing AI

Visual Testing AI is the use of artificial intelligence to automate and enhance visual testing, identifying UI defects by comparing rendered images against baselines, improving accuracy and reducing manual effort.