Learn About AI

A/B testing (AI) refers to the application of A/B testing methodologies to develop, evaluate, and refine artificial intelligence models and AI-driven features, or the use of AI to enhance the A/B testing process itself.

The term “AI agent” describes a software entity that can perceive its environment, make decisions based on goals or objectives, and take actions that alter the state of the world.

AI batch processing is an approach that enables the asynchronous execution of large groups of artificial intelligence (AI) tasks, providing significant gains in throughput, cost efficiency, and scalability.

AI compute refers to the specialized computational resources, infrastructure, and processing power required to train, develop, and deploy artificial intelligence systems.

The AI darkside refers to the potential negative consequences, ethical challenges, and unintended harmful impacts that can emerge from artificial intelligence technologies.

AI data connectors are the bridge-builders of the artificial intelligence world. They help AI systems access, retrieve, and integrate data from various sources. They serve as the crucial bridges that allow AI applications to communicate with databases, APIs, files, and other data repositories, turning raw info into something AI can actually work with.

AI development is fundamentally about discovery - a messier, more experimental process than traditional software development that combines science, engineering, and art to learn whether something can be built at all, and if so, how.

AI ethics is the branch of applied ethics that examines the moral implications and societal impact of creating and using artificial intelligence. It is the systematic study of the ethical questions that arise as we design, deploy, and live alongside increasingly intelligent and autonomous systems.

An AI ethics framework provides the structured approach organizations need to identify, evaluate, and address the moral implications of their artificial intelligence systems.

AI fog computing is a way of running artificial intelligence on smaller computers that sit between your local devices (like security cameras or factory sensors) and the big cloud data centers.

AI gateways act as hubs that transform fragmented technologies—like legacy systems, AI models, and siloed data repositories— into cohesive, functional ecosystems. Instead of systems operating in isolation, gateways ensure they interact smoothly and efficiently.

AI Heuristics focus on “good enough” outcomes that balance speed with practicality. This approach enables AI to adapt dynamically to real-world constraints, making decisions that are fast, efficient, and often remarkably effective in scenarios where perfection is unnecessary or unattainable.

AI hybrid cloud is the strategy that combines public clouds, private clouds, and on-premises infrastructure, allowing companies to run their AI applications in the best possible place for that specific job, giving them a flexible, powerful, and secure way to build the future of intelligence.

AI model governance provides the oversight necessary to manage risks, build trust, and align AI with societal priorities.

AI model optimization is the ongoing process of refining machine learning models to enhance their accuracy, reliability, efficiency, and overall operational effectiveness.

AI multi-cloud is the strategy of using the best services from several different cloud providers to build and run your AI applications, rather than committing to just one.

Natural Language Processing (NLP) is the branch of artificial intelligence that gives computers the ability to understand, interpret, and generate human language in a way that's both meaningful and useful. Think of it as teaching machines to read your texts, understand your voice commands, and even write you back—not with robotic, stilted responses, but with language that feels natural and human.

AI networking refers to the specialized communication infrastructure that connects computing resources, storage systems, and distributed components in artificial intelligence environments. It encompasses the hardware, protocols, and architectures designed to handle the unique data movement patterns and performance requirements of AI workloads.

AI on-premises is the practice of running artificial intelligence systems entirely within an organization's own data centers and infrastructure, rather than using cloud-based AI services, giving companies complete control over their data, models, and computing resources.

AI runtime is the specialized software environment that takes a trained machine learning model and makes it work efficiently in real-world applications, handling everything from optimization and hardware adaptation to serving predictions to users.

AI serverless is a cloud computing approach that allows developers to deploy and run artificial intelligence applications without managing the underlying server infrastructure, where AI models and functions automatically scale based on demand and users pay only for the computational resources actually consumed during execution.

AI storage is the specialized infrastructure designed to handle the massive datasets, extreme performance demands, and unique access patterns of artificial intelligence workloads throughout their lifecycle.

AI strategies are comprehensive frameworks that guide how organizations adopt, implement, and manage artificial intelligence technologies to achieve specific objectives. They're not just technical roadmaps—they're the bridge between cutting-edge AI capabilities and real-world value creation.

By tuning a single numeric value, you can shape your AI’s “voice” to be factually grounded or daringly imaginative. This single dial helps balance accuracy against imagination, making it an essential lever for tailoring AI to various tasks, from official statements to exuberant marketing copy.

AI virtualization creates abstracted computing environments that allow artificial intelligence workloads to run independently of the underlying physical hardware.

AI as a Service (AIaaS) is the practice of using ready-made artificial intelligence tools and capabilities from a third-party provider over the internet, much like you’d stream a movie instead of owning the DVD.

These systems integrate advanced technologies like natural language processing (NLP) and machine learning (ML) to automate tasks, analyze risks, and streamline reporting processes.

The term “AI-complete”—also known as AI-hard—refers to tasks or problems considered as difficult as achieving general human-like intelligence.

API authentication is the process of verifying the identity of users, applications, or systems attempting to access AI services through Application Programming Interfaces.

API authorization determines what actions authenticated users or applications can perform when accessing AI services and resources.

An API gateway for AI is a specialized middleware platform that sits between your applications and artificial intelligence services, managing the complex dance of requests, responses, and resources that make modern AI systems work.

API logging for AI systems captures the detailed interactions, performance metrics, and operational telemetry that flow between applications and artificial intelligence services.

API management for AI is the specialized practice of governing how artificial intelligence services are exposed, secured, monitored, and scaled through Application Programming Interfaces.

API monitoring for AI systems is the continuous observation and analysis of how artificial intelligence applications communicate, perform, and behave through their programming interfaces.

API rate limiting is the practice of controlling how many requests a user, application, or system can make to an API within a specific time period.

ASIC acceleration is the use of custom-built chips designed to do one thing and do it with breathtaking efficiency.

Abstraction in AI refers to the structuring of logic into higher-level, reusable representations that allow both developers and models to operate over complexity without handling every detail explicitly.

Adapter tuning is a method that allows a massive, general-purpose AI model to learn a new, specific skill by adding and training only a tiny set of new components, leaving the vast majority of the original model untouched.

The Adaptive Neuro-Fuzzy Inference System (ANFIS)—also known as Adaptive Network-based Fuzzy Inference System—is a powerful computational model that seamlessly blends fuzzy logic with artificial neural network methods.

Adversarial attacks—targeted manipulations designed to make a model misbehave—first gained academic attention in the early 2000s with efforts to bypass spam filters, but their significance has skyrocketed as machine learning has become more deeply embedded in critical systems.

Adversarial robustness is a measure of an AI model's ability to withstand subtle, malicious inputs and still make correct predictions. A robust model is one that doesn't just perform well in the sterile, predictable environment of a lab; it remains reliable and trustworthy when deployed in the messy, unpredictable real world, where adversaries may be actively trying to deceive it.

Adversarial testing is a security practice where AI systems are intentionally challenged with malicious or deceptive inputs designed to make them fail.

AI Alignment is the ongoing effort to ensure that advanced AI systems pursue goals and behave in ways that are consistent with human intentions, preferences, and ethical principles.

Ambient intelligence (AmI) is a vision of technology that seamlessly blends into our everyday environments, responding to our presence, anticipating our needs, and adapting to our preferences—all without requiring explicit commands or interaction.

Annoy (Approximate Nearest Neighbors Oh Yeah) is a lightweight, open-source library developed by Spotify that revolutionized similarity search by trading perfect accuracy for dramatic speed improvements, enabling real-time nearest neighbor queries across massive high-dimensional datasets.

AI is about creating machines that can do things that normally require human thinking: learning, reasoning, recognizing patterns, and making decisions.

Assistant prompts are the specific instructions, questions, or requests that users provide to AI assistants to guide their responses and behavior in conversational contexts.

Attention mechanism is a technique that gives AI models the ability to focus, to weigh the importance of different pieces of information, and, in doing so, to understand context in a way that has completely revolutionized fields like natural language processing.

Audit logging is the systematic recording of activities, decisions, and events within AI systems to create a comprehensive trail of what happened, when it happened, and who was involved.

AI auditability refers to the capability to examine, verify, and evaluate artificial intelligence systems to ensure they're functioning as intended, following ethical guidelines, and complying with regulations.

Authentication in AI systems is the process of verifying the identity of users, applications, or other AI agents before granting access to resources, data, or services.

While authentication asks "who are you?", authorization answers the equally critical question "what are you allowed to do?"

Automated machine learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning to real-world problems, from data preparation to model deployment, making it possible to build high-quality models with minimal human intervention.

AI automation combines artificial intelligence capabilities with automated systems to create technologies that can learn, adapt, and improve over time while performing tasks that were previously done by humans.

An autonomous agent is an AI-powered system capable of making decisions and performing actions independently to achieve specific goals. They gather real-time data, evaluate possible actions based on programmed rules or learning models, and execute decisions to adapt to dynamic environments.

In simple terms, AI Availability is all about making sure our AI systems are ready, accessible, and actually doing their job whenever we need them to – think of it as the AI equivalent of having the lights on and someone being home and ready to answer the door.

Batch inference emerges as a strategic alternative that handles large workloads in scheduled intervals.

Batch learning, often referred to as offline learning, is one of the earliest and most common paradigms in machine learning. Traditionally, the model-building process assumes you have access to a static, complete dataset: everything you need to train an accurate model is gathered, then you fit a model on the entirety of that data in one go.

Think of Behavior Trees as the ultimate decision-making cheat sheet for AI. They're like organized flowcharts that help AI decide what to do next based on what's happening around them.

AI benchmarks are standardized tests designed to provide a common yardstick that allows researchers, companies, and users to compare different AI systems objectively and track progress in the field.

Bias detection is the process of systematically examining artificial intelligence systems to find and measure instances where they produce unfair or prejudicial outcomes for different groups of people.

CI/CD for ML is the practice of using automated processes to build, test, and release machine learning models into real-world applications.

CTransformers is a lightweight, developer-friendly library that brings Transformer models to laptops, edge devices, and offline environments—no cloud required.

Canary deployment is a software release strategy where a new version of an application is gradually rolled out to a small subset of users or servers before making it available to the entire user base.

Certified robustness is a formal guarantee that a machine learning model's output will not change for a given input, even when that input is perturbed within a specific, predefined range.

AI compliance involves systematically ensuring that artificial intelligence systems meet applicable laws, regulations, ethical guidelines, and industry standards throughout their lifecycle—from design and development to deployment and ongoing operation. It's about building AI that's not just powerful, but also trustworthy, fair, and safe.

Containerization is the art of bundling an application with everything it needs to run—all its dependencies like software libraries, system tools, the actual code, and runtime settings—into one neat, isolated, executable package.

Content filtering is the automated process of analyzing, categorizing, and controlling digital content using artificial intelligence to determine what material should be displayed, restricted, or removed based on predefined policies and safety criteria.

A contextual prompt enriches a directive with extra information or background so the resulting output is more relevant and accurate. By providing context—like the user’s role, the conversation history, or domain-specific references—these prompts can tailor an LLM’s behavior far more effectively than a bare one-liner.

In the world of artificial intelligence, contextual recall refers to the ability of AI systems to retrieve and utilize information based on the surrounding context, allowing them to access relevant knowledge at the right time and in the right situation.

Cost Monitoring is the systematic process of tracking, measuring, and analyzing every dollar that flows through your AI operations.

AI cost optimization refers to the systematic approach of maximizing the efficiency and effectiveness of artificial intelligence systems while minimizing expenses associated with their development, deployment, and operation.

Direct Preference Optimization (DPO) is a training method for refining language models based on human preferences. It works by learning from a dataset where humans have selected the better of two responses to a given prompt.

Data parallelism is a strategy for training a single AI model by splitting a massive dataset across multiple processors, like a team of chefs all cooking the same recipe but each with their own portion of the ingredients.

Data preprocessing is the essential first step of transforming raw, messy, and often incomplete data into a clean, consistent, and understandable format that machine learning models can effectively learn from.

AI data security is the specialized and multi-faceted discipline of protecting the data used and processed by artificial intelligence systems throughout their entire lifecycle, from initial collection to eventual deletion.

Data validation is the rigorous process of ensuring that all data is clean, accurate, and fundamentally fit for its intended purpose by verifying it against a set of predefined rules and standards before it is used or analyzed.

Deep learning is a type of machine learning that uses multi-layered artificial neural networks to automatically learn patterns and representations from large amounts of data. The approach is fundamentally about learning by example, but on a massive scale.

Dense vectors are like a universal translator that converts the messy, complex world of human concepts into the precise mathematical language that AI systems can understand and work with.

Differential privacy is a formal mathematical framework that allows data analysts and machine learning models to learn from a dataset while providing a strong, provable guarantee that the privacy of any single individual in that dataset is protected.

Distributed training is the practice of splitting the massive job of training an AI model across multiple computers, or “nodes,” which work together to get the job done faster.

Drift detection is the practice of identifying when and how a model’s performance is degrading over time.

The encoder-decoder architecture is a way of organizing AI systems into two parts: one part that reads and understands the input, and another part that uses that understanding to create the output.

Error rate monitoring tracks how often AI systems make mistakes, providing the essential feedback loop that keeps artificial intelligence reliable and trustworthy.

Explainable AI (XAI) is a set of processes and methods that allow human users to comprehend and trust the results and output created by machine learning algorithms. It's the critical discipline focused on demystifying the so-called "black box" of AI, ensuring that the systems we build are not only powerful but also transparent, fair, and accountable.

It transforms raw data—like images, text snippets, or transaction records—into feature embeddings, enabling quick retrieval without brute-forcing every comparison.

FPGA acceleration is the use of field-programmable gate arrays to speed up computational workloads, particularly those in artificial intelligence and machine learning.

Factual accuracy in AI refers to the ability of artificial intelligence systems to provide information that is correct, verifiable, and corresponds to established facts in the real world.

AI fairness is the ongoing effort to ensure that machine learning algorithms and the automated systems they power do not create or perpetuate unfair biases against individuals or groups, particularly those in legally protected or otherwise vulnerable categories.

Feature embeddings are numerical representations that convert complex data—such as text, images, audio, or code—into machine-readable formats that AI models can analyze. Think of embeddings as a map where data points are plotted based on their relationships; and AI uses this map to find patterns and make predictions.

Feature engineering is the process of transforming raw data into meaningful features that help machine learning models perform better.

Feature vectors are the numerical fingerprints of data, transforming raw information into structured representations that algorithms can analyze, compare, and learn from. By encoding the attributes and relationships of data into numerical values, feature vectors allow AI systems to identify patterns, classify data points, and make predictions with precision.

Federated learning is a machine learning approach where a shared model is trained across many different devices or servers, without the training data ever leaving those devices. Instead of collecting all the data in one central place, the AI model is sent out to where the data lives.

Few-shot learning is a machine learning technique that enables large language models (LLMs) to adapt to new tasks with minimal data. This approach eliminates the need for extensive retraining, allowing models to generalize effectively from just a handful of examples. The result is a system that is faster to deploy and more resource-efficient, even in data-scarce environments.

Few-shot prompting is a strategy for steering large language models (LLMs) using a handful of examples. The idea is that by seeing a couple of cases, the model can infer the general pattern and apply it to a new query.

Function calling is the ability for large language models to invoke external tools, APIs, and services to accomplish tasks that require real-time information, computation, or interaction with external systems.

Function calling is what allows LLMs to go beyond conversation and actually execute actions. Instead of just describing how to complete a task, the model produces a structured command—typically in JSON—that an external system can execute.

GPT function call represents a sophisticated capability that allows large language models to connect with external tools, APIs, and systems, transforming them from conversational partners into active agents capable of performing real-world tasks.

GPU acceleration refers to the use of a Graphics Processing Unit (GPU) in conjunction with a Central Processing Unit (CPU) to speed up scientific, engineering, and artificial intelligence applications. By offloading compute-intensive portions of an application to the GPU, while the remainder of the code still runs on the CPU, complex tasks can be processed much faster.

A GPU cluster is a team of specialized computer processors all working together on the same problem.