{"id":107310,"date":"2023-01-24t11:57:10","date_gmt":"2023-01-24t16:57:10","guid":{"rendered":"\/\/www.g005e.com\/?p=107310"},"modified":"2024-08-07t23:09:53","modified_gmt":"2024-08-08t03:09:53","slug":"vector-databases-the-newest-tool-for-the-ai-era","status":"publish","type":"post","link":"\/\/www.g005e.com\/2023\/01\/24\/vector-databases-the-newest-tool-for-the-ai-era\/","title":{"rendered":"vector databases: the newest tool for the ai era"},"content":{"rendered":"

<\/strong><\/p>\n

rapid data searches and production-ready features can be game changers.<\/strong><\/p>\n

by rick richardson<\/em>
\n<\/strong>technology this week<\/a>\u00a0<\/em><\/p>\n

making data-driven decisions is becoming increasingly understood by companies in every industry as a requirement for competing today, in the next five years, in the next 20 years and beyond. according to <\/span>current market research<\/span><\/a>, the worldwide artificial intelligence (ai) market will “increase at a compound annual growth rate (cagr) of 39.4 percent to reach $422.37 billion by 2028,” driven by the exponential expansion of unstructured data in particular.<\/span><\/p>\n

more tech this week: <\/b>russia-linked ransomware back with a vengeance<\/a> | amazon aws: the mainframe killer?<\/a> | amazon launching its first internet satellites<\/a> | russian solarwinds hackers at it again<\/a> | nasa finds a $10 quintillion asteroid<\/a> | firms must balance benefits, risks of emerging technology<\/a> | microsoft and google go to war<\/a>
\n\"goprocpa.com\"exclusively for pro members. <\/span><\/strong>log in here<\/a> or 2022世界杯足球排名 today<\/a>.<\/span><\/p><\/blockquote>\n

the era of data overload and ai has arrived, and there is no turning back.<\/span><\/p>\n

<\/p>\n

this reality implies that ai can truly sift and handle the deluge of data \u2013 not just for big giants like alphabet, microsoft and meta with their massive r&d departments and tailored ai tools, but also for the typical corporation and even some small and medium-sized businesses.<\/span><\/p>\n

well designed ai-based systems quickly filter through enormously vast datasets to produce fresh insights, which fuel fresh sources of income, adding significant value to enterprises. but without the new kid on the block, <\/span>vector databases<\/span><\/a>, none of the data expansion becomes operationalized and democratized. vector dbs represent a paradigm shift in database management and a new category for using the exponential amounts of unstructured data currently untapped in object stores. in particular, vector databases provide a mind-numbing new degree of search capacity for unstructured data, but they can also handle semi-structured and structured data.<\/span><\/p>\n

vectors and search.\u00a0<\/span><\/strong>unstructured data, which can’t be simply sorted into row and column relationships, rarely matches the relational database paradigm. unstructured data management methods that are incredibly time-consuming and unreliable frequently include manually labelling the data (think labels and keywords on video platforms). examples include photos, video, audio and user actions.<\/span><\/p>\n

the real problem is that human methods make it very hard to perform a semantic search that comprehends the context and meaning of a picture or other unstructured piece of data, in addition to a search query.<\/span><\/p>\n

enter embedding vectors, often known as feature vectors, vector embeddings or just embeddings. they are numerical values, or coordinates, that represent unstructured data features or objects, such as a part of a picture, a section of a person’s purchasing history, a few frames from a video, geospatial information or anything else that doesn’t neatly fit into a relational database table. these embeddings enable scalable, snappy \u201csimilarity search.\u201d<\/span><\/p>\n

quality data and insights.\u00a0<\/span><\/strong>an ai model, or a machine learning (ml) or deep learning model, trained on very large amounts of high-quality input data, produces embeddings as a computational byproduct. a model is the computational result of an ml algorithm (method or procedure) conducted on data to further draw crucial distinctions. sophisticated, widely used algorithms include <\/span>stego<\/span><\/a>\u00a0for computer vision,\u00a0<\/span>cnn<\/span><\/a>\u00a0for image processing and\u00a0<\/span>google\u2019s bert<\/span><\/a> for natural language processing. the resulting models turn each single piece of unstructured data into a list of floating-point values \u2013 our search-enabling embedding.<\/span><\/p>\n

therefore, a properly trained neural network model will produce embeddings consistent with particular content and may apply to a semantic similarity search. a vector database, specifically designed to manage embeddings and their unique structure, is the instrument to store, index and search through these embeddings.<\/span><\/p>\n

the fact that developers from everywhere may now incorporate a vector database into an ai system, with its production-ready features and lightning-fast unstructured data search, is crucial in the industry.<\/span><\/p>\n

the concept of vector search has been around for quite a while, but only on a very small scale. many businesses aren’t accustomed to having access to the kind of data mining and search capabilities that contemporary vector databases provide. teams sometimes struggle with knowing where to begin. therefore, their creators continue to focus on spreading the word about how they operate and why they are valuable. organizationally, a crucial component of standardizing the usage of vector databases is assisting business teams and their leadership in understanding why and how they can benefit.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

rapid data searches and production-ready features can be game changers.<\/strong>
\n\"\"<\/a>
\nby rick richardson<\/em>
\n<\/strong>technology this week<\/a>\u00a0<\/em><\/p>\n","protected":false},"author":1373,"featured_media":105707,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","footnotes":""},"categories":[3120,3002,2306],"tags":[3445,3447,629],"class_list":["post-107310","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-pro-member-exclusive","category-special","category-tech-and-fintech","tag-ai","tag-artificial-intelligence","tag-technology"],"acf":[],"yoast_head":"\nvector databases: the 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