{"id":4471,"date":"2026-03-26T11:59:07","date_gmt":"2026-03-26T09:59:07","guid":{"rendered":"https:\/\/dataverse.lv\/konference-izstade-ai-big-data-expo-global-2026\/"},"modified":"2026-03-26T12:01:07","modified_gmt":"2026-03-26T10:01:07","slug":"konference-izstade-ai-big-data-expo-global-2026","status":"publish","type":"post","link":"https:\/\/dataverse.lv\/en\/konference-izstade-ai-big-data-expo-global-2026\/","title":{"rendered":"Conference-Exhibition AI &#038; Big Data Expo Global 2026"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1248px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:1.92%;--awb-margin-bottom-large:100px;--awb-spacing-left-large:1.92%;--awb-width-medium:100%;--awb-order-medium:0;--awb-spacing-right-medium:1.92%;--awb-spacing-left-medium:1.92%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\" data-scroll-devices=\"small-visibility,medium-visibility,large-visibility\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-text fusion-text-1\"><p>On 4\u20135 February 2026, the international conference-exhibition <a href=\"https:\/\/www.ai-expo.net\/global\/\">AI &amp; Big Data Expo Global 2026<\/a>, organised by <a href=\"https:\/\/tecex.com\/?utm_term=techex+global&amp;utm_campaign=TecEx+Brand+%28Search%29&amp;utm_source=adwords&amp;utm_medium=ppc&amp;hsa_acc=7305057376&amp;hsa_cam=21046440433&amp;hsa_grp=160002197940&amp;hsa_ad=691519363803&amp;hsa_src=g&amp;hsa_tgt=kwd-483030570886&amp;hsa_kw=techex+global&amp;hsa_mt=e&amp;hsa_net=adwords&amp;hsa_ver=3&amp;gad_source=1&amp;gad_campaignid=21046440433&amp;gbraid=0AAAAABY4llEgkEzEczY4DegnyeQJ5Z9k0&amp;gclid=CjwKCAiAwNDMBhBfEiwAd7ti1AcA2rL3Lt7QoBOWvMPB2_a0jmrdbcRFhPJs7LgK0bCOAEaWiJi0JBoC2U0QAvD_BwE\">TechEx<\/a>, took place in London, UK, bringing together more than 8,000 participants. The event was attended by three representatives of the Latvian Data Curators\u2019 Network from the Latvia University of Life Sciences and Technologies, who explored current trends in artificial intelligence (AI), data governance and infrastructure.<\/p>\n<p>The discussions reflected a shift from experimentation with AI toward structured and governed implementation, with a stronger emphasis on value, security and accountability.<\/p>\n<p><strong>Key Insights<\/strong><\/p>\n<ul>\n<li>Data-First approach. The quality of AI systems is directly dependent on data quality. Without structured, consistently described and validated datasets, even powerful models produce unstable and difficult-to-audit results. This implies that datasets must be \u201cAI-ready\u201d, with comprehensive metadata, clear provenance and quality controls.<\/li>\n<li>GenAI and research automation. Generative AI is increasingly used for text, code and workflow automation. This increases the need to clearly distinguish between primary data, derived data and AI-generated content, as well as to ensure precise documentation of data provenance.<\/li>\n<li>Responsible and Explainable AI (XAI). In the academic context, it is essential to understand and justify AI-generated outputs. This is a prerequisite for trustworthy AI use in research.<\/li>\n<li>Agentic AI. The use of specialised AI agents in data processing is expanding. As such tools operate rapidly and at scale, it becomes critical to establish formal standards, validation mechanisms and appropriate oversight.<\/li>\n<li>Skills development and AI literacy. Technological progress outpaces human skills. Universities need to strengthen AI literacy among staff and students, including the ability to critically evaluate AI outputs and effectively design prompts.<\/li>\n<li>Data security as a foundational requirement. Most AI security issues arise not from sophisticated cyberattacks but from insufficient data classification, labelling and lifecycle management. Well-established basic practices are essential for secure AI deployment.<\/li>\n<li>Sustainable AI. The high energy consumption associated with AI model training and maintenance is significant. To reduce the environmental impact of research and technological development, greater attention must be given to energy-efficient algorithms, optimised model usage and responsible resource planning.<\/li>\n<li>Collaborative ecosystems. AI development increasingly relies on cross-sector partnerships that combine academic expertise, industrial technological competence and public sector experience in developing shared solutions.<\/li>\n<li>The insights gained during the event confirm that the quality of AI solutions is directly linked to the quality of data. The reliability of AI outputs depends on accurate information and comprehensive metadata (licensing, access conditions, versioning), which ensure auditability and reusability.<\/li>\n<\/ul>\n<p>Future development directions in this context include strengthening consistent data deposition practices in the national data repository, reinforcing metadata completeness, clear licensing and data provenance as minimum requirements. It is also important to clearly distinguish primary data, derived data and AI-generated content and to further develop a user-friendly environment with clear templates, an understandable submission process and timely feedback.<\/p>\n<p>The implementation of AI in academia involves both technical solutions and clearly defined responsibilities within a unified institutional approach. When data governance is established as a shared institutional competence, AI solutions can provide practical support for research and teaching while maintaining scientific quality and reliability.<\/p>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>On 4\u20135 February 2026, the international conference-exhibition AI &#038; Big Data Expo Global 2026, organised by TechEx, took place in London, UK, bringing together more than 8,000 participants.<\/p>\n","protected":false},"author":2,"featured_media":4468,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4471","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/dataverse.lv\/en\/wp-json\/wp\/v2\/posts\/4471","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dataverse.lv\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dataverse.lv\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dataverse.lv\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dataverse.lv\/en\/wp-json\/wp\/v2\/comments?post=4471"}],"version-history":[{"count":2,"href":"https:\/\/dataverse.lv\/en\/wp-json\/wp\/v2\/posts\/4471\/revisions"}],"predecessor-version":[{"id":4473,"href":"https:\/\/dataverse.lv\/en\/wp-json\/wp\/v2\/posts\/4471\/revisions\/4473"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dataverse.lv\/en\/wp-json\/wp\/v2\/media\/4468"}],"wp:attachment":[{"href":"https:\/\/dataverse.lv\/en\/wp-json\/wp\/v2\/media?parent=4471"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dataverse.lv\/en\/wp-json\/wp\/v2\/categories?post=4471"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dataverse.lv\/en\/wp-json\/wp\/v2\/tags?post=4471"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}