{"id":238433,"date":"2025-12-17T11:00:00","date_gmt":"2025-12-17T10:00:00","guid":{"rendered":"https:\/\/digitalproduction.com\/?p=238433"},"modified":"2025-12-18T12:25:57","modified_gmt":"2025-12-18T11:25:57","slug":"smpte-updates-its-ai-report-a-roadmap-for-media-engineers","status":"publish","type":"post","link":"https:\/\/digitalproduction.com\/2025\/12\/17\/smpte-updates-its-ai-report-a-roadmap-for-media-engineers\/","title":{"rendered":"SMPTE updates its AI report: a roadmap for media engineers"},"content":{"rendered":"\n<p><em>For those who don&#8217;t know the Smpte: The <a href=\"https:\/\/www.smpte.org\/\" title=\"\">Society of Motion Picture and Television Engineers (SMPTE)<\/a> sets the technical standards behind nearly everything that moves on a screen. From colorimetry to file formats, SMPTE defines the backbone of professional workflows in postproduction, broadcast, and digital cinema. Their new engineering <a href=\"https:\/\/www.smpte.org\/blog\/smpte-releases-updated-engineering-report-on-artificial-intelligence-and-the-media\" title=\"\">report Artificial Intelligence and Media (ER 1011:2025)<\/a> updates the 2023 edition with the latest thinking on AI, and is freely available to non-members.<\/em><\/p>\n\n\n\n<h3 id=\"open-source-and-open-ai\" class=\"wp-block-heading\">Open source and open AI<\/h3>\n\n\n\n<p>The first section revisits the foundations of <em>machine learning<\/em> and <em>deep learning<\/em>, before expanding into what SMPTE now calls \u201copen source artificial intelligence\u201d. The report clarifies the difference between <em>open-weight<\/em> and truly <em>open source<\/em> AI, where not just the model weights, but also code, data, and documentation are publicly accessible. It also points to the emerging concept of <em>Responsible AI Licenses<\/em> and frameworks like the <em>Model Openness Framework (MOF)<\/em>. SMPTE notes that \u201copen-washing\u201d, marketing closed systems as open, is a growing issue engineers must recognise.<\/p>\n\n\n\n<h3 id=\"generative-models-and-interoperability\" class=\"wp-block-heading\">Generative models and interoperability<\/h3>\n\n\n\n<p>A substantial chapter examines <em>generative AI<\/em>, from <em>diffusion models<\/em> and <em>GANs<\/em> to <em>LLMs<\/em> and multimodal architectures capable of handling text, sound, image, and video simultaneously. The report highlights the growing influence of protocols like the <em>Model Context Protocol (MCP)<\/em> and Google\u2019s <em>A2A<\/em> for <em>agent-to-agent<\/em> interoperability, described as the foundation for \u201cagentic workflows.\u201d SMPTE sees these protocols as key to connecting intelligent tools in post and broadcast environments, without locking developers into single ecosystems.<\/p>\n\n\n\n<h3 id=\"security-ethics-and-regulation\" class=\"wp-block-heading\">Security, ethics, and regulation<\/h3>\n\n\n\n<p>Security receives dedicated attention. SMPTE warns of risks such as <em>data poisoning<\/em>, <em>jailbreaking<\/em>, and <em>intellectual property leakage<\/em> within AI-driven workflows. The report advocates <em>Zero Trust Architectures<\/em> (as defined by <em>MovieLabs<\/em>), calling for authentication and authorisation mechanisms even between AI agents. The ethics chapter, meanwhile, argues that AI in media must be both transparent and auditable \u201cbecause failing is expensive, and media needs its own voice.\u201d It outlines principles of inclusivity, transparency, and fit-for-purpose design, forming what SMPTE calls the \u201cAI Ethics Pipeline\u201d.<\/p>\n\n\n\n<h3 id=\"standards-and-data-the-next-frontier\" class=\"wp-block-heading\">Standards and data: the next frontier<\/h3>\n\n\n\n<p>In its closing chapters, the report maps the <em>AI standards landscape<\/em>, noting gaps around <em>ontologies<\/em>, <em>benchmarking<\/em>, and <em>model metadata<\/em>. The authors identifie opportunities for collaboration with the <em>EBU<\/em>, <em>ISO\/IEC<\/em>, and other bodies, stressing the need for shared <em>datasets<\/em> to train and validate media-specific AI systems. These datasets, the report says, are crucial for reproducible research and safe deployment in production pipelines. As always, SMPTE closes with a reminder that technology, especially AI, must prove itself in real-world conditions before being trusted in production.<\/p>\n\n\n\n<p><br \/><\/p>\n\n\n\n<p>The full <em>Engineering Report ER 1011:2025 \u2013 Artificial Intelligence and Media<\/em> is available for free download from the <em><a href=\"https:\/\/www.smpte.org\/blog\/smpte-releases-updated-engineering-report-on-artificial-intelligence-and-the-media\" title=\"\">SMPTE website<\/a><\/em>. It is dense, detailed, and refreshingly un-hyped. A technical must-read for anyone designing, coding, or approving AI-assisted tools in post, broadcast, or digital cinema. Before your next machine learning experiment runs in production, take an hour to read what SMPTE\u2019s engineers have already tested and defined.<\/p>\n","protected":false},"excerpt":{"rendered":"SMPTE\u2019s revised AI report defines where AI stands in media: open source, agentic systems, security, ethics, and data. Read before you automate.\n","protected":false},"author":163,"featured_media":241739,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"csco_singular_sidebar":"","csco_page_header_type":"","csco_appearance_grid":"","csco_page_load_nextpost":"","csco_post_video_location":[],"csco_post_video_location_hash":"","csco_post_video_url":"","csco_post_video_bg_start_time":0,"csco_post_video_bg_end_time":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"SMPTE just rewrote the manual on AI in media. From ethics to open source to MCP, it\u2019s the report every VFX-Worker should actually 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