AI glossary for IT leaders

Artificial intelligence

Artificial intelligence (AI) is an umbrella term that encompasses various technologies designed to mimic human intelligence. It enables computers to effectively “think,” “learn,” and “create.” This set of technologies includes machine learning (ML), natural language processing (NLP), deep learning, neural networks, generative AI, and large language models (LLMs).

• What it means for customers: Customers interact with AI daily through smart assistants, recommendation engines, search engines, chatbots, and automated services. AI enhances user experiences by making applications more personalized, efficient, and intuitive.
• What it means for IT leaders: AI is redefining this business and technology. You can leverage AI to automate workflows, improve decision-making, and streamline business processes. Optimize your data stores for more efficacy and efficiency. From predictive analytics to cybersecurity, AI helps organizations drive innovation, reduce costs, and improve operational efficiency.

Algorithm

An algorithm is a mathematical set of finite instructions. In the context of AI, a machine learning algorithm is the “step-by-step guide” a computer will use to complete a task — generally, a data processing task.

• What it means for customers: Algorithms power all the AI and machine learning applications that users interact with daily, such as recommendation engines and AI assistants.
• What it means for IT leaders: Algorithms are the heart of data processing tasks, machine learning, and AI. AI requires multiple algorithms to function.

AI bias

AI bias refers to the bias (prejudice) AI systems inherit from their training data and the humans involved in training them. Training data, especially public data, can contain a variety of sociocultural biases that can skew outputs and negatively impact certain individuals or groups.

• What it means for customers: Any interaction with AI, in particular generative AI, may return biased results. Users must exercise critical thinking when interacting with AI and using AI to make decisions.
• What it means for IT leaders: AI bias awareness is essential in the responsible use and creation of AI applications. Different strategies like employing diverse teams, using diverse datasets, and deploying “blind taste tests” help to mitigate bias in AI.

AI ethics

AI ethics is an emerging and evolving field of study that investigates the ethical stakes of AI. AI ethics examines questions of bias, fairness, privacy, regulation, accountability, and automated decision-making.

• What it means for customers: AI ethics say that AI and algorithms should be used to improve, not harm. This means eliminating biases around race, gender, politics, etc. — not reinforcing them. Individuals should be made aware of the potential risks and benefits of any experiment that they’re a part of, and they should be able to choose to participate or withdraw at any time before and during the experiment.
• What it means for IT leaders: Ensure the governance and guardrails you use to build your AI systems cover ethical considerations. This includes ensuring privacy, taking accountability for what you create, educating employees on responsible ways to use and build AI models, communicating the risks involved with using AI, and more.

AI security and privacy

When crawling and scraping through data, AI poses security and privacy concerns. AI can scrape personal and proprietary data. Generative AI can train itself on private data and make your personal content public or even commit fraud, manipulation, or nonconsensual likenesses.

• What it means for customers: Customers should be made aware of any risks of using AI tools, as well as be provided with options or warnings to mitigate risks.
• What it means for IT leaders: Take necessary steps to prevent security and privacy threats before and after deploying a product.

Big data

Big data refers to massive structured, semi-structured, and unstructured datasets that grow quickly over time and thus require advanced data management and processing systems, such as machine learning algorithms.

• What it means for customers: Big data is the basis of personalized customer experiences, storing user preferences to predict user behavior and create recommendations. When all these data points are pooled, big data can be analyzed for large-scale customer sentiment or trend predictions.
• What it means for IT leaders: Big data is a continuous challenge for IT leaders, who must parse through the noise to obtain actionable insights that benefit their organizations. AI is emerging as an invaluable tool for IT leaders in the processing and analysis of big data.

Conversational AI

Conversational AI refers to a type of generative AI that relies on machine learning and natural language processing to enable conversational interactions between computers and users.

• What it means for customers: Conversational AI allows customers to use conversational language to engage with computers, enhancing customer support and search experiences.
• What it means for IT leaders: Organizations can rely on conversational AI to provide more meaningful and personalized service to their customers, while improving organizational efficiency with faster and more intuitive enterprise search.

Generative AI

Generative AI relies on deep learning, large language models, and neural networks to generate new and original content. It achieves this by learning to recognize patterns in data and predicting results based on those patterns when prompted.

• What it means for customers: Generative AI can be used in chatbots to provide personalized and conversational service to customers.
• What it means for IT leaders: Generative AI can improve productivity and efficiency by helping developers produce code, security analysts create threat intelligence reports at scale, and support engineers problem-solve complex customer problems.

Natural language processing

Natural language processing (NLP) refers to the set of machine learning algorithms and techniques that enable computers to understand and communicate in human (or natural) language.

• What it means for customers: Any query you type into a search engine that is interpreted, understood, and that produces related results relies on NLP.
• What it means for IT leaders: NLP is at the core of human-machine interaction, so it’s a vital part of many efficiency and productivity-enhancing AI tools, such as semantic search, chatbots, and virtual assistants.

Neural network

A neural network is a machine learning subset, in which nodes are organized in neural layers that mimic the structure of the human brain.

• What it means for customers: Neural networks are integral to sentiment analysis, a data analysis process that allows companies to understand their interactions with large sets of customers. This helps organizations improve their services and products and better meet the needs of consumers.
• What it means for IT leaders: Neural networks are crucial parts of intensive data processing and analysis, as well as sophisticated decision-making and pattern recognition. Neural networks are also better suited to handling noisy data, making them better for at-scale complex problem-solving.

Structured data

Structured data is highly organized information, typically stored in rows and columns, like in spreadsheets or databases. Think: metrics, dates, names, zip codes, and credit card numbers. AI can easily analyze structured data because it follows a fixed format, allowing for quick sorting, filtering, and statistical analysis.

• What it means for customers: Because it is so easily analyzed, structured data allows for fast data retrieval. For example, it lets customers get recommendations for products based on past purchases.
• What it means for IT leaders: Structured data is easy to use, store, and scale, and it can simplify data mining. It lends itself more easily to forecasts, predictions, and studies, producing accurate business intelligence.

Unstructured data

Unstructured data includes text, images, and audio and doesn’t follow a fixed structure. It is qualitative rather than quantitative, stored in its native format. The actionable insights aren’t obvious. It has to be converted and analyzed separately.

• What it means for customers: Unstructured data often contains rich, detailed information that creates better digital experiences when user journeys are mapped and analyzed.
• What it means for IT leaders: As the most prevalent kind of data, unstructured data requires additional storage and analytic resources. It is rich in customer, operational, security, and business insights, which you can leverage if you can analyze it. Natural language processing and machine learning help to manage this type of data.

Deep learning

Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to model complex patterns in data. It excels at processing unstructured data like images, text, and audio.

• What it means for customers: Customers experience improved AI services, such as personalized recommendations, voice assistants, and image recognition.
• What it means for IT leaders: Although deep learning requires significant computational resources, IT leaders can leverage deep learning to enhance automation, predictive analytics, and decision-making.

Federated learning

Federated learning is a decentralizing AI model training method. It enhances privacy by keeping data localized while still enabling collaborative learning.

• What it means for customers: Customers benefit from AI-powered applications such as personalized recommendations or predictive text on mobile devices, without their personal data being sent to central servers.
• What it means for IT leaders: IT leaders can implement AI solutions that comply with data privacy regulations while still improving models through distributed learning. This approach is particularly useful in industries like healthcare and finance, where sensitive data cannot be shared.

Machine learning

Machine learning (also supervised and unsupervised) is a branch of artificial intelligence that uses data and algorithms to process data and learn from it, gradually improving over time. Supervised learning refers to machine learning models that are trained using labeled datasets, while unsupervised learning models are trained on data that hasn’t been labeled.

• What it means for customers: Customers interact with machine learning daily, from personalized product recommendations to spam filters in email. The more data the model processes, the better the experience becomes.
• What it means for IT leaders: IT leaders can use machine learning to enhance business intelligence, automate decision-making, and optimize processes. Choosing between supervised and unsupervised methods depends on data availability and business goals.

Meta learning

Meta learning is a form of machine learning that learns to learn. In other words, meta learning is what enables adaptive AI — models that learn new tasks and adapt on their own.

• What it means for customers: Meta learning is used for recommendation engines to help deliver relevant and personalized information and services. From user actions, the model can learn and adapt for future recommendations.
• What it means for IT leaders: Meta learning is key to advanced automation. Meta learning creates very adaptable AI models that are especially suited to robotics, computer vision, and natural language processing applications.

Fine-tuning

Fine-tuning is a step in the process of training machine learning models that involves adapting pre-trained models to specific use cases. By refining a model on a smaller, domain-specific dataset, fine-tuning improves performance for specialized applications while leveraging the knowledge of the original model.

• What it means for customers: Fine-tuning enables AI-powered applications to deliver more accurate and relevant experiences. For example, fine-tuned language models can provide industry-specific chatbots, personalized recommendations, or improved speech recognition tailored to different accents.
• What it means for IT leaders: Fine-tuning allows IT leaders to optimize AI models for their organization's needs without requiring massive datasets or computational resources. This approach enhances efficiency and ensures AI solutions align with business objectives. It’s less costly than pre-training an LLM but more costly than using retrieval augmented generation.

Pre-training

Pre-training is a resource-intensive approach includes starting from scratch by training a large language model on a large set of data. The advantage of this is that you create a model that is perfectly tuned to your organization’s needs and data. The disadvantage is that it takes a load of time and a load of budget.

• What it means for customers: When an organization pre-trains its own LLM, it has full control over customers’ data privacy. For example, a pre-trained LLM could ensure that a healthcare organization’s data practices are aligned with frameworks like GDPR or HIPAA, no need to be concerned with third-party models.
• What it means for IT leaders: This is the most expensive (both in budget and time) model technique there is. You are creating an LLM from scratch! Questions to ask yourself before you go down this route: Do you have a large enough dataset to provide the LLM with significant learning material? If not, will you combine public and private data?

Agentic AI

Agentic AI is emerging as the next big thing in AI. Unlike generative AI that responds to user-created prompts, agentic AI acts independently. It observes, reasons, plans, and executes complex, multi-step tasks — enabling faster decisions to improve customer experiences, increase compliance, track anomalies, and more.

• What it means for customers: Agentic AI can guide customers through processes, like applying for a loan or troubleshooting network connectivity.
• What it means for IT leaders: Because agentic AI systems pursue goals, choose tools, and adapt as conditions change, they’re well-suited for customer-facing experiences. By taking on lower-level cases, agentic AI will free up support engineers’ time so they can focus on more complex issues.

AIOps

AIOps (artificial intelligence for IT operations) refers to the application of AI and machine learning to automate and enhance IT operations. AIOps is used to help detect anomalies, predict system failures, and optimize performance.

• What it means for customers: Customers experience more reliable digital services with fewer disruptions. AIOps helps prevent outages, reduce downtime, and ensure smooth online experiences.
• What it means for IT leaders: IT leaders can use AIOps to automate incident detection and response, improve system efficiency, and reduce operational costs, enabling teams to focus on strategic initiatives rather than routine maintenance.

AI assistants, chatbots, and copilots

AI assistants, chatbots, and copilots are AI-powered tools that interact with users through natural language, providing information, automating tasks, and offering real-time support. Copilots specifically enhance productivity by assisting users with complex workflows.

• What it means for customers: Customers receive instant, personalized support through AI-driven assistants in customer service, shopping, and productivity applications. Chatbots and copilots streamline interactions, reducing wait times and improving user experience.
• What it means for IT leaders: IT leaders can deploy AI assistants to create self-serve experiences for customer support, enhance employee productivity, and improve knowledge management. These tools reduce operational costs while increasing efficiency and engagement.

AI agents

AI agents are advanced AI systems designed to perform tasks autonomously by making decisions, acting independently, and learning from feedback. Unlike chatbots, AI agents operate with a higher degree of independence and adaptability.

• What it means for customers: AI agents can proactively assist with tasks, from scheduling meetings to managing personal finances. AI agents benefit customers by anticipating needs and taking action without constant input.
• What it means for IT leaders: IT leaders can implement AI agents to automate complex workflows, optimize business processes, and drive efficiency across operations. AI agents help organizations scale intelligent automation beyond simple task execution.

AI playground

An AI playground is a virtual testing space akin to a sandbox, in which developers can safely prototype AI models, techniques, and tools.

• What it means for customers: AI playgrounds help organizations provide customers with safe, reliable, and cutting-edge AI tools and services.
• What it means for IT leaders: IT leaders can leverage AI playgrounds for prototyping and testing AI applications before full-scale deployment. These environments enable teams to refine AI models and optimize performance with minimal risk.

Agentic workflows

Agentic workflows involve AI systems working autonomously or collaboratively to complete multistep processes with minimal human intervention. These workflows enhance efficiency by enabling AI to handle tasks dynamically.

• What it means for customers: Customers experience faster and more intelligent automation in services. For example, agentic workflows can track, order, and monitor products to prevent items from being out of stock.
• What it means for IT leaders: IT leaders can implement agentic workflows to streamline operations, reduce manual workload, and enhance AI-driven decision-making. This approach boosts productivity and scalability across industries.

ChatGPT

ChatGPT is a conversational generative AI model developed by OpenAI that generates human-like responses to text inputs. It is used for chatbots, content creation, coding assistance, and more.

• What it means for customers: Customers can engage with ChatGPT for instant answers, creative writing assistance, and AI-driven recommendations.
• What it means for IT leaders: IT leaders can integrate ChatGPT into applications to improve customer support, automate knowledge retrieval, and enhance team productivity. Note: ChatGPT is trained on publicly accessible internet data, which can be a privacy concern.

Edge AI

Edge AI refers to AI models that process data locally on devices rather than relying on centralized cloud servers.

• What it means for customers: Customers benefit from real-time information from their smart devices (e.g., security cameras, sensors) for timely insights.
• What it means for IT leaders: IT leaders can leverage edge AI to deploy intelligent solutions that work efficiently in low-latency environments. This is especially valuable for industries like manufacturing, healthcare, and autonomous systems, where real-time decision-making is crucial.

Enterprise search

Also known as corporate search software, enterprise search is a solution for finding data and information within an enterprise organization. It is used for search applications such as web, ecommerce, knowledge bases, and customer service apps.

• What it means for customers: With external enterprise search, like company search bars, customers can quickly find content on an enterprise organization’s website.
• What it means for IT leaders: Internal enterprise search is used by teams to access business information, documents, and all the nuts and bolts of your tech stack, data, and logs.

Human-in-the-loop

This technique refers to machine learning workflows that require human intervention for oversight and feedback. This ensures AI systems remain accurate, ethical, and aligned with human expertise.

• What it means for customers: Customers benefit from AI-driven services that are more accurate and trustworthy.
• What it means for IT leaders: Most automation workflows currently require human-in-the-loop to enhance reliability and ensure compliance with industry standards. This approach is particularly useful in fields like healthcare, finance, and content moderation, where human expertise is essential.

Job automation

Job automation refers to the use of AI and machine learning to streamline or fully automate repetitive and manual tasks, increasing efficiency and reducing human workload.

• What it means for customers: Customers benefit from faster services, such as automated customer support, instant loan approvals, and AI-driven scheduling assistants.
• What it means for IT leaders: IT leaders can optimize workforce productivity by automating repetitive tasks, allowing employees to focus on higher-value work. While automation improves efficiency, leaders must also consider workforce reskilling and change management.

Large language model

A large language model (LLM) is a deep learning algorithm that is trained on vast amounts of text data to perform a variety of natural language processing tasks.

• What it means for customers: Customers interact with LLMs in various AI applications, such as ChatGPT, AI search assistants, and content creation tools. These models enable more natural and context-aware AI conversations.
• What it means for IT leaders: IT leaders can integrate LLMs into business applications to enhance customer interactions, automate knowledge management, and improve productivity. Selecting the right LLM requires balancing accuracy, cost, and ethical considerations.

Prompt engineering

Prompt engineering is the practice of designing and refining prompts, or inputs, to optimize large language models (LLMs) and other AI systems’ responses, or outputs.

• What it means for customers: Customers benefit from AI that better understands their needs and delivers more accurate responses.
• What it means for IT leaders: IT leaders can leverage prompt engineering to enhance AI applications without extensive retraining. By refining prompts, organizations can improve AI performance, reduce costs, and accelerate deployment, making AI tools more reliable and aligned with business goals.

Retrieval augmented generation (RAG)

Retrieval augmented generation, or RAG, is a machine learning technique that involves feeding proprietary data to models for improved accuracy in specific use cases.

• What it means for customers: Customers benefit from more personalized and relevant enterprise search experiences.
• What it means for IT leaders: IT leaders can integrate RAG into enterprise AI systems to improve knowledge retrieval, enhance customer support, and ensure AI-generated content is backed by reliable sources.

Predictive analytics

Predictive analytics refers to the process of using AI and machine learning models to make predictions about future events, trends, or behaviors. Predictive analytics help organizations anticipate outcomes and optimize decision-making.

• What it means for customers: Thanks to predictive analytics, customers benefit from personalized experiences, such as tailored product recommendations, proactive customer service, and predictive maintenance alerts.
• What it means for IT leaders: IT leaders can leverage predictive analytics to improve forecasting, enhance risk management, and take a proactive stance.

Search AI

Search AI merges search technology with generative AI to transform information retrieval and augmentation by prioritizing retrieval over retraining. Search AI allows organizations to pass on the most relevant information to AI models, improving their accuracy.

• What it means for customers: Search AI overcomes generative AI challenges by providing factual and up-to-date search query results.
• What it means for IT leaders: Search AI helps IT leaders overcome the lack of transparency and accountability in generative AI applications by combining search’s capability to retrieve context-aware data with natural language processing. This enables organizations to build next-generation AI tools that can solve complex problems faster.

Search AI lake

Search AI lake is a vast, low-latency, cloud-native storage solution designed for Search AI applications. It stores structured and unstructured data and enables organizations to index and retrieve information efficiently.

• What it means for customers: A Search AI lake enables Search AI, which improves generative AI search experiences.
• What it means for IT leaders: Search AI lakes enhance data accessibility by enabling separate computing and storage, durable and inexpensive object storage, as well as low-latency querying at scale.

Sentiment analysis

Sentiment analysis is an AI-driven technique that evaluates text, speech, or social media data to determine the emotional tone — positive, negative, or neutral — behind communications. It helps organizations understand customer opinions and market trends.

• What it means for customers: Sentiment analysis helps brands respond to concerns and enhance customer satisfaction.
• What it means for IT leaders: IT leaders can deploy sentiment analysis to monitor brand reputation, analyze customer feedback at scale, and improve decision-making. By understanding sentiment trends, organizations can refine marketing strategies and product development.

AI in operations enhances efficiency by automating routine tasks, optimizing resource allocation, and providing predictive insights for proactive decision-making. AI In cybersecurity strengthens defenses by detecting anomalies, identifying potential threats in real time, and enabling rapid response to mitigate risks.

AI automation

AI automation refers to the use of AI and machine learning to automate tasks, optimize processes, and make decisions with minimal or no human intervention. It combines AI models with workflow automation to increase efficiency and scalability.

• What it means for customers: AI automation enhances user experiences by reducing wait times and improving accuracy.
• What it means for IT leaders: IT leaders can implement AI automation to streamline operations, reduce costs, and free up employees for higher-value tasks. By automating repetitive workflows, organizations can improve productivity and innovation.

AI security analytics

AI security analytics uses AI to detect and analyze cybersecurity threats in real time, enhancing traditional security systems by identifying anomalies, patterns, and potential attacks faster than manual methods.

• What it means for customers: Thanks to ongoing AI security analytics, customers benefit from improved security, reduced risk of data breaches, and faster responses to cyber threats.
• What it means for IT leaders: IT leaders can leverage AI security analytics to strengthen threat detection, automate incident response, and reduce the burden on security teams.

AI SIEM

AI-driven SIEM (security information and event management) integrates AI analytics into traditional SIEM systems to enhance threat detection, analysis, and incident response. AI SIEM systems process vast amounts of security data in real time to identify and mitigate risks.

• What it means for customers: AI SIEM ensures that businesses can quickly detect and respond to security threats, safeguarding sensitive customer information.
• What it means for IT leaders: IT leaders can use AI SIEM to automate security monitoring, detect threats more efficiently, and improve workflows by eliminating blind spots.

MLOps

MLOps is a framework that helps organizations streamline the deployment of machine learning models in production environments. It ensures machine learning models remain reliable, scalable, and maintainable over time.

• What it means for customers: MLOps ensures AI-driven applications perform consistently and with minimal downtime.
• What it means for IT leaders: IT leaders can implement MLOps to standardize and automate the lifecycle of machine learning models, reducing operational challenges. By integrating MLOps, organizations can deploy machine learning solutions more efficiently and at scale.

Operational resilience

Operational resilience refers to the ability of an organization to withstand and bounce back from disruptions, such as cyber attacks, system failures, and market shifts. Improving operational resilience means improving the ability to mitigate, detect, respond, and quickly recover from operational disruptions.

• What it means for customers: Customers can rely on an organization to consistently deliver products or services, even during disruptions like cyber attacks or system failures. Operational resilience ensures continuity in service and minimal impact on customer experience.
• What it means for IT leaders: Operational resilience is an imperative goal for organizations — it determines a company’s ability to ensure business continuity. IT leaders can rely on AI automation to improve their monitoring capabilities and response times.

Security analytics

Security analytics includes the process of collecting and analyzing security event data to improve threat detection and overall security. Security analytics are used to mitigate attacks and cyber threats.

• What it means for customers: Security analytics helps ensure that services are secure and reliable and that sensitive data (such as personal information, passwords, and more) is protected.
• What it means for IT leaders: Security analytics ensures faster mean-time-to-response (MTTR) during security events, contributing to operational resilience and reduced operational costs. Security analytics enhanced with AI can process large and varied datasets in real time, making it an essential tool for organizations.

Telemetry data

Telemetry data is the real-time data produced by an organization's systems, processes, and applications. It includes logs, metrics, traces, and profiles. Telemetry data is collected and analyzed to monitor performance, security, and usage patterns.

• What it means for customers: Telemetry data is used by organizations to detect issues, monitor performance, and improve service reliability, improving overall customer experience.
• What it means for IT leaders: IT leaders can harness telemetry data for modern observability, improved security, and enhanced performance. Telemetry data can provide IT leaders with actionable insights, driving better decision-making and operational efficiency.

Threat hunting

Threat hunting is a proactive cybersecurity practice in which security analysts actively search, or hunt, for cyber threats with the help of AI-powered tools.

• What it means for customers: Customers benefit from enhanced security, as organizations can detect and stop cyber threats before they impact personal data or digital services. Proactive threat hunting reduces the risk of fraud and cyber attacks.
• What it means for IT leaders: IT leaders can integrate AI-driven threat hunting and threat intelligence reporting to identify hidden risks, strengthen defenses, and improve incident response times. Automating threat hunting with AI enhances cybersecurity resilience and reduces potential damage from cyber attacks.

AI is revolutionizing search and retrieval by enabling smarter, faster, and more accurate results through natural language processing (NLP) and machine learning.

Information retrieval

Information retrieval is the process of retrieving information from large semi-structured and unstructured datasets. Search engines rely on information retrieval processes in order to efficiently deliver relevant responses.

• What it means for customers: Customers can quickly access accurate and relevant information from vast datasets, like finding precise answers through search engines. It enhances customers’ experiences by saving time and providing tailored results based on their queries.
• What it means for IT leaders: Information retrieval represents the implementation of advanced systems and algorithms to manage, search, and analyze large volumes of data efficiently. It enables them to optimize organizational processes, improve decision-making, and deliver better user experiences through effective data utilization.

Search relevance

Search relevance is a process that analyzes and rates search result relevance with linguistic analysis, ranking algorithms, and contextual factors.

• What it means for customers: Search relevance ensures that the results produced by search engines are relevant and match the query. This process is key in delivering satisfactory search experiences.
• What it means for IT leaders: Search relevance helps IT leaders ensure the quality of search application output, so that results match both the query and its intent.

Semantic search

Semantic search is a search technology that goes beyond keyword matching to understand the meaning of natural language queries. Semantic search uses machine learning and AI to deliver more relevant search results.

• What it means for customers: Because semantic search applications can interpret meaning, customers benefit from more intuitive search experiences.
• What it means for IT leaders: IT leaders can implement semantic search to improve knowledge discovery, optimize customer self-service capabilities, and enhance internal search tools. This leads to increased efficiency and better decision-making.

Text classification

Text classification refers to the use of AI to classify text into predefined context, sentiment, and content categories. It is used in the context of sentiment analysis or information filtering (e.g., distinguishing spam from regular emails).

• What it means for customers: Customers benefit from more relevant and organized content, such as accurate email spam filters, personalized recommendations, and improved customer support automation.
• What it means for IT leaders: IT leaders can use text classification to automate customer service triage and perform sentiment analysis. Text classification helps organizations process large amounts of data and draw actionable insights from it, enhancing efficiency.

Vector database

A vector database is a database designed to store vectors, which are numerical representations of data objects. Vector databases are data management solutions and are crucial to similarity search applications like recommendation engines and image recognition.

• What it means for customers: Customers experience faster, more accurate recommendations in services like ecommerce and streaming platforms that use vector databases.
• What it means for IT leaders: Vector databases are at the heart of most AI models, enabling a variety of applications such as indexing, similarity search, and distance metrics.

Vector embeddings

Vector embeddings, sometimes referred to as vectors or embeddings, are a way to represent data objects such as images, words, sentences, videos, and documents numerically. Vector embeddings are used to understand relationships between data points and are crucial parts of search applications.

• What it means for customers: Vector embeddings make vector search possible. Vector search enables more efficient and accurate search and retrieval of information, helping customers get the answers they need quickly.
• What it means for IT leaders: Machine learning algorithms rely on vector embeddings to find patterns in data and perform tasks such as sentiment analysis, language translation, recommendation systems, and many more.

Vector search

Vector search uses machine learning, vector databases, and vector embeddings to find similar items based on meaning, rather than relying on exact keyword matches.

• What it means for customers: Customers experience more accurate and relevant search results, whether looking for similar products, finding related articles, or searching by images instead of text.
• What it means for IT leaders: IT leaders can implement vector search to enhance AI-driven discovery, making it easier to retrieve relevant information across large datasets. This is essential for improving search accuracy in AI applications.

Word embeddings

Word embeddings refers to the technique of representing words in numbers so computers can understand them. In the context of natural language processing, word embeddings represent words as vectors, allowing computers to learn the relationship between words and make predictions.

• What it means for customers: Word embeddings are the kernel technology of human-to-machine communication. They enable computers to understand natural language and, in various use cases, create more intuitive interactions between customers and computers.
• What it means for IT leaders: Word embeddings are key to AI’s language processing abilities.