Sed ut perspiciatis unde. Processing, storing, and retrieving vast amounts of information rapidly and efficiently is paramount for businesses. Vector databases are a critical emerging technology in addressing this demand. Unlike traditional databases, vector databases focus on high-dimensional vector data, offering unique advantages for certain use cases. Businesses and leaders that use emerging technology such as LLM and Generative AI or plan to invest in a project involving such technology need to understand vector databases. This article will delve into vector databases’ use cases, exploring their most prevalent applications and why it’s becoming indispensable for many industries.Given the high-dimensional nature of images and videos, vector databases are naturally suited for tasks like similarity search within visual data. For instance, companies with vast image databases can use vector databases to find similar images, facilitating tasks like duplicate detection or image categorization.Consider a platform like Pinterest. Users often pin images without detailed descriptions. A vector database can represent each image as a high-dimensional vector. When a user pins an image of a coastal sunset, the system can search through its vector database to suggest similar images, perhaps other beach landscapes or sunsets, enhancing content discovery and user engagement.In Natural Language Processing (NLP), words or sentences can be represented as vectors through embeddings. With vector databases, finding semantically similar texts or categorizing large volumes of textual data based on similarity becomes feasible, becoming apparent in the Semantic Analysis step (Figure 1).Figure 1: How Does NLP Work? 1For example, in a customer support chatbot system, customer queries are transformed into vectors using embeddings. When a user asks, “How do I reset my password?” the vector database can identify semantically similar queries like “Steps for password change” to provide a relevant response even if the exact phrasing isn’t in the system.Whether for movies, music, or e-commerce products, recommendation systems often rely on understanding the similarity between user preferences and item features. Vector databases can accelerate this process, making real-time, personalized recommendations a reality.For example, on Netflix, movies and TV shows are represented as vectors based on their genres, actors, and user reviews. When a user watches a psychological thriller starring a particular actor, the vector database can suggest other movies in the same genre or films with the same actor, offering a tailored viewing experience. The ‘Top Picks for X’ section we encounter in most streaming platforms are concrete examples. For example, the author of this article watches political TV shows often, and Netflix advises him to watch House Of Cards. See Figure 2.Figure 2: ‘Top Picks’ Feature on NetflixFrom face recognition systems to fingerprint databases, biometric data is high-dimensional and requires efficient similarity search capabilities. Similarly, anomaly detection in systems like network security can benefit from vector databases, where “normal” patterns are vectors, and deviations or anomalies can be quickly identified.For example, at an international airport, a facial recognition system is used for security concerns. Each passenger’s face is captured and converted into a vector. When a passenger approaches the security check, their face is matched against a vector database of known criminals or persons of interest, ensuring rapid threat detection.Check our list for biometric authentication software. In the medical and pharmaceutical fields, molecules and genes can be represented as high-dimensional vectors. Searching for similar compounds or genetic patterns is much more efficient when utilizing a vector database. For example, chemical compounds are represented as high-dimensional vectors in a pharmaceutical research lab. When researchers identify a compound promising in treating a specific disease, the vector database can find other compounds with similar structures or properties, potentially leading to more efficient drug discovery processes.Discover other AI applications in the healthcare and pharma industry.High-dimensional data can arise from portfolios, trading patterns, or risk profiles in finance. Vector databases enable rapid similarity searches, which is beneficial for fraud detection or portfolio management tasks.For example, user transaction patterns are represented as vectors in a digital banking platform. If a user typically makes small, local purchases and suddenly there’s a large international transaction, the system’s vector database can quickly identify this as an anomalous pattern, flagging it for potential fraud investigation.Imagine an e-commerce platform that sells clothing. A high-dimensional vector can represent each product based on various attributes like color, style, fabric, and customer reviews. When a user browses a product, the system can quickly search the vector database to find items with similar attributes, offering personalized product suggestions. Over time, this leads to a tailored shopping experience, potentially boosting sales and customer satisfaction. 90% of customers emphasize spending more with companies that personalize their customer service for them.Check our list for e-commerce personalization software. Vector databases are used extensively in the healthcare industry; one of the wide uses is patient similarity analysis. According to analysis, the total revenue opportunity for the healthcare AI market will exceed $34 billion by 2025.In a hospital setting, patient records, including symptoms, medical history, and genetics, can be transformed into vectors. If a doctor is treating a patient with a rare set of symptoms, the vector database can identify past patients with similar profiles, enabling the doctor to consider previously effective treatments or identify potential risk factors.On a music streaming platform like Spotify, each song can be represented as a vector based on features such as genre, rhythm, melody, and instrumentals. When a user listens to a jazz song with a particular tempo and mood, the platform can use the vector database to suggest other tracks with a similar vibe, enhancing the user experience. Figure 3: Spotify Discover WeeklyIf you have further questions about use of AI and its application in various industries, reach us:Find the Right VendorsAltay is an industry analyst at AIMultiple. He has background in international political economy, multilateral organizations, development cooperation, global politics, and data analysis.He has experience working at private and government institutions. Altay discovered his interest for emerging tech after seeing its wide use of area in several sectors and acknowledging its importance for the future. He received his bachelor’s degree in Political Science and Public Administration from Bilkent University and he received his master’s degree in International Politics from KU Leuven .Source link Save my name, email, and website in this browser for the next time I comment.By using this form you agree with the storage and handling of your data. * Δdocument.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() );Tech dedicated news site to equip you with all tech related stuff.I agree that my submitted data is being collected and stored.✉️ Send us an emailTechToday © 2023. All Rights Reserved.TechToday.co is a technology blog and review site specializing in providing in-depth insights into the latest news and trends in the technology sector.TechToday © 2023. 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