AI in the Museum: Connecting Futures

A Structural Shift in the Architecture of the WebA structural shift is unfolding beneath the surface of the web. For three decades, websites have been designed primarily for human navigation. Pages were structured visually. Interfaces were optimized for clarity and engagement. Search engines indexed content so that users could find it, read it and act upon it.Today, a new layer is emerging. It is not designed for human eyes, but for artificial agents.The idea is simple, yet transformative. Instead of forcing AI systems to imitate human behavior by clicking buttons and interpreting graphical interfaces, websites can expose structured functions directly. Search the archive. Register for an event. Book a ticket. Retrieve metadata. Access educational material. These actions can be declared as callable tools, accessible to machines through standardized protocols. The web would no longer be merely read by machines. It would be executed by them.From Interface Imitation to Structured ExecutionUntil now, AI agents have had to “pretend” to be human users. They scrape pages, simulate cursor movements, interpret layouts and attempt to infer meaning from visual structures. This approach is fragile and inefficient. It depends on interface stability and often breaks when websites change.Emerging standards such as the Model Context Protocol and related experimental initiatives aim to move beyond this paradigm. They propose a structured interaction model in which publishers define the functions that agents are authorized to call, under controlled conditions. Instead of navigating through layers of design intended for humans, an agent can directly invoke a declared function and receive a structured response.This evolution does not signal the disappearance of graphical interfaces. Museums will continue to design websites for visitors. But alongside the human-facing layer, a second layer may develop. This new layer would expose services in a structured, machine-readable way. In practical terms, the web becomes two-layered. One layer serves human experience. The other serves machine execution.From Visibility to Actionability. The Rise of AEOIn such an environment, the strategic question shifts. For years, institutions have focused on Search Engine Optimization. The objective was visibility. How do we rank. How do we appear in search results. How do we attract traffic.In an agent-mediated ecosystem, visibility alone may no longer be sufficient. The new challenge becomes actionability. Can an AI assistant directly query our collection database. Can it enroll a user in a workshop. Can it retrieve authoritative descriptions without distorting them.This shift is sometimes described as a move from SEO to Agent Engine Optimization, or AEO. The question is no longer only whether agents can find us, but whether they can act through us in a controlled and reliable manner.Why This Matters for MuseumsFor museums, the implications are substantial. If AI assistants increasingly mediate access to information, they may become dominant gateways to cultural content. A visitor might ask an assistant to suggest exhibitions on climate change, retrieve primary sources on a historical event, or book a science workshop for a school group.If the museum’s digital infrastructure exposes structured services, the assistant can perform these actions directly and accurately. If it does not, the assistant will rely on secondary sources, approximations or aggregated data from platforms that do not belong to the institution.The stakes extend beyond convenience. They concern the circulation of knowledge. Museums are custodians of validated, curated and contextualized information. When AI agents become intermediaries, the integrity of that information depends on how it is accessed and transmitted. Structured exposure of functions allows institutions to define the rules of interaction. They can specify what is accessible, under what conditions, with what attribution requirements. They can embed source references, usage constraints and traceability mechanisms into the interaction layer itself.Governance, Sovereignty and Editorial ControlThis introduces a governance dimension. Who defines the callable functions? Which datasets are exposed? How is premium or subscription-based content protected? How is editorial value preserved?Archives, digitized collections and scholarly resources represent significant investments. If agents can retrieve and redistribute content without clear attribution or control, institutional authority may erode. Conversely, if museums define interoperable standards and maintain oversight of their machine-facing layer, they can strengthen their position within AI ecosystems.The discussion is therefore not purely technical. It touches on sovereignty. If large AI platforms define proprietary interaction standards, cultural institutions risk dependency. Their content could circulate primarily through external ecosystems, shaped by opaque algorithms. Open and standardized approaches to an executable web offer an alternative path. They enable institutions to remain actors rather than passive data providers. By declaring structured tools themselves, museums can retain agency in how their knowledge is mobilized.Rethinking Digital Strategy in an Agent-Mediated WorldThis transformation also redefines metrics of digital success. Traffic and page views may no longer capture the full picture. A museum’s influence might increasingly be measured by how often its structured services are called by trusted agents, how frequently its authoritative descriptions are cited, and how effectively its systems integrate into educational or research workflows mediated by AI.Yet caution is necessary. An executable web amplifies both opportunity and risk. Poorly defined interfaces could expose sensitive data. Insufficient traceability mechanisms could weaken attribution. Over-automation could reduce the richness of human engagement. Museums must therefore approach this shift deliberately. The question is not whether to embrace or reject machine interaction, but how to design it in alignment with institutional values.At a practical level, cultural institutions may begin by identifying the services that could benefit from structured exposure. Collection search functions. Event registration systems. Educational resource access. Metadata retrieval. Each of these can be mapped and evaluated. Which actions should be callable. Which require authentication. Which must remain restricted. This mapping exercise is as much strategic as technical.Conclusion. Becoming Executable on Our Own TermsThe emergence of an executable web marks a profound reconfiguration of digital infrastructure. It suggests that websites are no longer static presentations of information, but programmable knowledge systems.Museums have long adapted to technological change. From print catalogues to websites, from audio guides to immersive installations, they have integrated new media into their mission of transmission. The agent-driven web represents another stage in this evolution. It challenges institutions to rethink their digital architecture, not as a mere communication channel, but as an interoperable system within a broader computational ecosystem.The critical question is therefore not whether machines will execute parts of the web. That trajectory is already visible. The real question is whether museums will shape this layer proactively, or allow it to be shaped around them. In an agent-mediated world, remaining visible is no longer enough. Institutions must also remain executable, on their own terms.Benjamin BENITA, Editor-in-Chief, MuseumWeek MagazineSources: Google previews WebMCP, a new protocol for AI agent interactionsAgents no longer need to “pretend” to be humanModel Context Protocol (MCP) Get full access to MuseumWeek Magazine at museumweek2h1r4.substack.com/subscribe

Generative AI and Museums. What Truly Creates Value for AudiencesGenerative artificial intelligence is gradually establishing itself in museums. Not only as a mediation tool, but also as a new way to explore collections and shape visitor experiences. Yet a central question remains for cultural institutions. What actually drives audiences to adopt these AI-powered tools.A recent academic study published in npj Heritage Science provides robust, data-driven answers, based on the analysis of The Living Museum, an experimental generative AI platform developed by the British Museum .A study grounded in real user experienceThe research draws on responses from 726 users, clearly distinguishing between cultural professionals and non-professional visitors.Its objective. To understand how perceived value is constructed and how it directly influences the intention to use generative AI in a museum context.The key factor. Relevance over spectacleThe main finding is unequivocal.Audience adoption is not driven by the abstract promise of AI, but by two very concrete capabilities:* Semantic relevance. The ability of the AI to provide accurate, meaningful answers aligned with users’ questions and expectations.* Contextual adaptability. The capacity to adjust responses according to the visitor’s level of knowledge, intent (casual exploration or in-depth inquiry), language, and situational context.In other words, an AI perceived as accurate and well-situated creates more value than an AI designed primarily to impress. For museums, this reinforces a critical principle. AI must strengthen cultural authority, not undermine it.What increases perceived valueFour factors significantly enhance perceived value:* Usefulness. Helping visitors understand, navigate, and explore collections more effectively.* Enjoyment. A smooth, engaging interaction that does not feel effortful.* Novelty. The feeling of discovering a new way to relate to heritage.* Relative advantage. Performing better than traditional tools such as labels, audio guides, or standard digital interfaces.By contrast, two elements clearly hinder adoption:* Perceived complexity, which disrupts immersion and generates cognitive fatigue.* Perceived risk, particularly regarding content reliability and data protection.One result is particularly striking. Explicit personalization does not significantly increase perceived value.In a museum context, visitors appear to prioritize scientific credibility and institutional trust over extensive configuration options. This insight has important implications for AI design in cultural settings.Perceived value drives adoptionThe study confirms a strong link between perceived value and intention to use.However, this relationship is moderated by two psychological factors:* Users with a high openness to innovation are more likely to translate positive experiences into sustained adoption.* Excessive interactivity can paradoxically weaken the impact of perceived value. When everything becomes interactive, clarity and depth may be lost.The message for museums is clear. More interaction is not always better. Balance matters.Professionals vs general audiences. Two distinct logicsThe research highlights a structural divergence between user groups:* Cultural professionals tend to value technological novelty and experimental potential.* General audiences are more sensitive to perceived risks and institutional guarantees.This implies differentiated strategies.A single AI system cannot be designed, framed, and deployed in the same way for all users.What this study changes for museumsThis research provides a clear framework for thinking about generative AI in museums:* AI is not primarily a technological issue, but a matter of cultural value perception.* Semantic accuracy, contextualization, and restrained interaction design are decisive.* Scientific authority and transparency become core design principles.* Strategies must be audience-specific, including at the interface level.For institutions engaged in MuseumWeek and beyond, this study serves as a valuable compass. It encourages museums to move beyond technological enthusiasm and toward a responsible, situated, and audience-centered approach to generative AI.Source: https://www.nature.com/articles/s40494-025-02194-9 Get full access to MuseumWeek Magazine at museumweek2h1r4.substack.com/subscribe

IntroductionAs part of our series exploring how AI is being implemented in museums worldwide, this case study focuses on the “Photo Detective” project at the Archäologisches Museum Hamburg (AMH). Led by Michael Merkel, this initiative tackles a common challenge for cultural institutions: managing vast collections of analogue photographs. While the AMH has digitized approximately 75% of its collection, the lack of detailed metadata makes these archives difficult for researchers and curators to navigate. Funded as a Proof of Concept by the InnotechHH Fund, “Photo Detective” uses automated tagging to transform these static images into a highly searchable digital resource.The Technological DimensionObject and Context Recognition The core of “Photo Detective” is an AI system driven by object recognition. The model was developed using a training set of 2,613 hand-annotated images. While the team considered 37 different object classes during the labeling phase, 21 classes were ultimately included in the final training model.The AI is capable of identifying a wide range of elements, from high-frequency subjects like “people” and “cars” to more specific architectural features like “timber framing,” “thatch roofs,” and “lattice windows.” Beyond identifying individual objects, the project explores context detection. For example, by recognizing specific clusters of objects—such as sports equipment or crowds—the AI can identify “sports events” as a general context. Remarkably, the technology’s potential extends beyond standard photography, successfully tagging historical engravings and postcards that include printed text.The “Human-in-the-Loop” Workflow A defining characteristic of this project is its “Human-in-the-Loop” centered workflow, which ensures that machine efficiency is balanced with human expertise. This six-step cycle creates a continuous loop of improvement:1. Data Annotation: Humans manually label training data in a dedicated application.2. Training: This data is used to teach the machine learning model.3. Evaluation: Professionals assess the model’s performance to ensure the quality of predictions.4. Hosting: The validated model is hosted on the “Photo Detective” platform.5. Bulk Processing: Users initiate the automated tagging of large datasets.6. Feedback: Ongoing user feedback is fed back into the database to refine future training phases.Impacts on the Cultural SectorThe implementation of “Photo Detective” is reshaping several areas of museum practice:• Institutional Management: By automating the labeling process, the museum significantly reduces the administrative burden of archival processing, allowing staff to focus on high-level curation.• Knowledge Sharing: In a move toward collaborative innovation, the AMH plans to make its training data available as “open data” for other cultural institutions, helping the wider sector develop similar tools.• Research and Mediation: Enhanced searchability allows researchers to find specific historical details, such as every image featuring a “horse” or a “shop window”, instantly, opening new doors for historical analysis and public engagement.Perspectives and IssuesThe “Photo Detective” project highlights the shifting role of the museum professional in the age of AI. While the tool offers immense speed, the “Human-in-the-Loop” approach is essential to address the opacity of machine-generated interpretations. It ensures that the final tags remain accurate and contextually relevant. Additionally, as museums become more dependent on these digital tools, questions regarding the long-term sustainability of the technology and the standardization of data across different institutions remain at the forefront of the discussion.ConclusionThe Archäologisches Museum Hamburg demonstrates how AI can revitalize historical archives. By combining automated object recognition with a rigorous human oversight process, “Photo Detective” makes history more accessible and participatory. This case serves as a model for how museums can use technology not just to store the past, but to make it a searchable and living resource for the 21st century.LinksArchäologisches Museum Hamburg: https://amh.deMichael Merkel on LinkedIn: https://www.linkedin.com/in/michael-merkel-8759bb13/ Get full access to MuseumWeek Magazine at museumweek2h1r4.substack.com/subscribe

You can listen to our podcast, read the full article, or watch the AI-generated video 📺 at the end of this page.Introduction – About the PodcastWelcome to the series Museum Professions: Working with AI, part of the AI in the Museum rubric by MuseumWeek. Each episode dives into a specific profession inside the museum world and explores how artificial intelligence is transforming daily practices. Today, we step into the shoes of a Cultural Project Manager.The Job and Its ChallengesCultural Project Managers oversee the planning, execution, and evaluation of projects within museums and cultural institutions. They are responsible for coordinating teams, managing budgets, and ensuring that projects meet organizational goals. Three major operational challenges include:1. Unpredictable audience engagement, which can lead to underwhelming attendance and financial shortfalls. 2. Budget management complexities, where unexpected costs can derail project timelines and objectives. 3. Stakeholder alignment, as diverse interests can create conflicts that hinder project progress.Advertisement (internal promotion by MuseumWeek).How AI Can Help – Practical Solutions with ToolsChallenge 1: Audience Engagement ForecastingThe Problem: Anticipating audience engagement is a significant challenge for cultural project managers. Inaccurate predictions can lead to insufficient resources allocated for marketing and programming, ultimately affecting attendance and revenue. Understanding audience preferences and behaviors is essential for tailoring experiences that resonate with visitors.AI Approach: Tools such as predictive analytics and natural language processing (NLP) can analyze historical attendance data and social media sentiment to forecast audience engagement. By identifying patterns and trends, cultural project managers can make data-driven decisions to enhance outreach and engagement strategies.Implementation Path: A project manager could begin by collecting historical attendance data and social media interactions. Using a predictive analytics tool like Tableau, they can visualize trends and create forecasts. By integrating NLP tools to analyze audience sentiment from social media, they can refine their marketing strategies and tailor programming to better meet audience expectations.Risks & Limits: While AI can provide valuable insights, it is essential to consider potential biases in data and the ethical implications of using audience data. Additionally, reliance on AI tools may lead to overconfidence in predictions, which could result in inadequate contingency planning.Recommended tools: - Tableau - Google Cloud AIChallenge 2: Budget ManagementThe Problem: Managing budgets in cultural projects can be fraught with uncertainties, as unexpected costs can arise at any stage. In a museum context, this can lead to compromised project quality or even project cancellation, impacting overall institutional goals.AI Approach: AI-driven financial modeling and anomaly detection tools can help project managers identify potential budget overruns before they occur. By analyzing historical spending patterns and real-time data, these tools can alert managers to unusual spending behaviors, allowing for proactive adjustments.Implementation Path: A cultural project manager could utilize an AI financial modeling tool such as Adaptive Insights to create a dynamic budget model that adjusts based on real-time data. By integrating this tool with existing financial systems, they can monitor spending closely and receive alerts for any anomalies, ensuring that budget management remains on track.Risks & Limits: The reliance on AI for budget management introduces risks related to data accuracy and the potential for algorithmic bias. Furthermore, the cost of implementing advanced AI tools may be prohibitive for smaller institutions, necessitating careful consideration of resource allocation.Recommended tools: - Adaptive Insights - IBM Planning AnalyticsChallenge 3: Stakeholder AlignmentThe Problem: Ensuring alignment among diverse stakeholders can be a complex task for cultural project managers. Conflicting interests and priorities may lead to project delays or failures, undermining the collaborative spirit essential in cultural institutions.AI Approach: Collaborative AI tools and sentiment analysis can facilitate stakeholder communication and gauge the overall sentiment towards project goals. By analyzing feedback and discussions, project managers can identify areas of concern and address them proactively.Implementation Path: A project manager could implement a sentiment analysis tool like MonkeyLearn to analyze stakeholder feedback gathered through surveys and meetings. By categorizing sentiments and identifying key concerns, they can tailor their communication strategies to address issues and foster alignment among stakeholders.Risks & Limits: While AI can enhance stakeholder engagement, it is crucial to ensure that the data collected respects privacy and ethical considerations. Additionally, over-reliance on automated tools may overlook the nuances of human communication, potentially leading to misunderstandings.Recommended tools: - MonkeyLearn - QualtricsLooking Ahead – Tomorrow’s PossibilitiesIn the next 12 to 24 months, cultural project managers can expect advancements in AI technologies to further enhance operational efficiency. Skills in data analysis and AI tool utilization will become increasingly important, necessitating training and development. However, governance frameworks will need to evolve to address ethical considerations and ensure equitable access to AI resources.ConclusionThis episode has highlighted the potential of AI in anticipating risks faced by cultural project managers. By leveraging predictive analytics, financial modeling, and sentiment analysis, project managers can enhance decision-making and foster resilience in their projects.Reflective Questions: 1. How can your institution better integrate AI tools to improve risk management practices? 2. What ethical considerations should be prioritized when implementing AI in cultural project management? 3. How can collaboration among different departments enhance the effectiveness of AI applications in your projects? Get full access to MuseumWeek Magazine at museumweek2h1r4.substack.com/subscribe

Introduction – About the PodcastWelcome to the series Museum Professions: Working with AI, part of the AI in the Museum rubric by MuseumWeek. Each episode dives into a specific profession inside the museum world and explores how artificial intelligence is transforming daily practices. Today, we step into the shoes of a Editorial webmaster.As the digital landscape evolves, editorial webmasters in museums face increasing pressure to manage content effectively while ensuring audience engagement. The integration of AI technologies presents both opportunities and challenges, as these professionals must navigate new tools and methodologies to enhance their workflows. Understanding the implications of AI on their roles is crucial for adapting to this rapidly changing environment.The Job and Its ChallengesEditorial webmasters are responsible for curating, managing, and updating digital content across museum platforms. They ensure that information is accurate, accessible, and engaging for diverse audiences. Key challenges include:1. Content Overload: The sheer volume of digital content can overwhelm webmasters, making it difficult to maintain quality and relevance. 2. Audience Engagement: Understanding and meeting the needs of varied audience segments is increasingly complex in a digital-first world. 3. Data Management: Efficiently managing and analyzing user data to inform content strategy poses significant challenges.How AI Can Help – Practical Solutions with ToolsChallenge 1: Content OverloadThe Problem: Museums often produce vast amounts of digital content, leading to potential information overload for both webmasters and users. This can result in outdated or irrelevant content, diminishing the user experience and engagement.AI Approach: AI can streamline content management through natural language processing (NLP) and recommender systems. These technologies can help prioritize and curate content based on user preferences and behavior.Implementation Path: A museum professional could implement an NLP tool to analyze existing content and identify which pieces are most frequently accessed or shared. By using a recommender system like Google Cloud Recommendations AI, they can personalize content suggestions for users, enhancing engagement. The workflow would involve inputting user interaction data, processing it through the AI tool, and outputting tailored content recommendations.Risks & Limits: While AI can significantly enhance content management, it may introduce biases based on historical data. Additionally, the cost of implementing advanced AI tools can be prohibitive for some institutions, and governance around data privacy must be carefully managed.Challenge 2: Audience EngagementThe Problem: Engaging diverse audiences requires a nuanced understanding of their interests and preferences, which can be difficult to ascertain without proper tools. Museums risk alienating segments of their audience if they fail to tailor content effectively.AI Approach: AI-driven analytics tools and chatbots can help gather insights on audience behavior and preferences. By leveraging these technologies, webmasters can create more targeted and engaging content.Implementation Path: A museum could deploy a chatbot, such as Dialogflow, to interact with visitors on their website. This chatbot can collect data on user inquiries and preferences, which can then be analyzed to inform content strategy. The workflow would involve setting up the chatbot, integrating it with the website, and using the collected data to refine content offerings.Risks & Limits: The use of chatbots raises concerns about user privacy and data security. Additionally, reliance on AI for audience engagement may overlook the importance of human interaction, which can be vital in a museum context.Challenge 3: Data ManagementThe Problem: Managing and analyzing user data effectively is crucial for informing content strategy, yet many webmasters lack the tools to do so efficiently. Poor data management can lead to missed opportunities for audience engagement.AI Approach: Machine learning algorithms and data visualization tools can assist in processing large datasets and extracting actionable insights. These technologies can help webmasters make informed decisions based on user interactions.Implementation Path: A museum could utilize a data visualization tool like Tableau to analyze user engagement metrics. By importing data from various sources, webmasters can create interactive dashboards that highlight trends and patterns in audience behavior. This enables them to adjust content strategies accordingly.Risks & Limits: The complexity of data governance can pose challenges, particularly regarding compliance with privacy regulations. Additionally, there is a risk of misinterpreting data without proper expertise, which could lead to misguided content decisions.Looking Ahead – Tomorrow’s PossibilitiesIn the next 12 to 24 months, the role of editorial webmasters will likely evolve as AI tools become more integrated into museum operations. Professionals will need to develop new skills in data analysis and AI tool management while ensuring ethical governance of user data. Opportunities for enhanced audience engagement and personalized content will grow, but institutions must remain vigilant about the potential constraints of technology reliance.ConclusionThis episode highlights the transformative impact of AI on the role of editorial webmasters in museums. By addressing challenges such as content overload, audience engagement, and data management, professionals can leverage AI to enhance their workflows and improve user experiences.Reflective questions for museum teams include: - How can we balance the use of AI tools with the need for human oversight in content management? - What strategies can we implement to ensure ethical governance of user data while utilizing AI technologies? Get full access to MuseumWeek Magazine at museumweek2h1r4.substack.com/subscribe

IntroductionAs part of our Series 1: Museums & Artificial Intelligence: Experimenting with Innovation, this case study explores how the National Palace Museum in Beijing has strategically adopted artificial intelligence to modernize its operations and engage audiences in new ways. The museum, one of the world’s largest repositories of Chinese cultural heritage, faces the challenges of mass tourism, limited physical access, and growing expectations for digital services. To respond, it has developed a suite of intelligent services that combine AI, big data, computer vision, and immersive technologies. The aim is not only to manage visitors more efficiently, but also to make collections more accessible, interactive, and sustainable—while strengthening the museum’s educational role.The Technological DimensionThe National Palace Museum applies AI across multiple layers of its visitor and operational ecosystem.* Intelligent ReceptionThe museum uses big data analytics coupled with AI-driven ticketing systems. Algorithms predict visitor flows based on historical attendance data, seasonality, and external factors (such as holidays or weather). Computer vision monitors crowd density and movement in real time through cameras placed in galleries and public spaces. These tools help regulate entry points, prevent overcrowding, and optimize staffing and security.* Intelligent ExhibitionsProjects like the Duanmen Digital Museum combine several technologies:* 3D animation and digital twins recreate historical spaces and artifacts with photorealistic detail.* Computer vision enables gesture recognition and object tracking, allowing visitors to interact with exhibits through movement or touchless controls.* Sensor-based systems (infrared, proximity, motion) trigger contextual content—such as audio narratives or lighting changes—when a visitor approaches. Together, these tools transform static displays into interactive, multisensory experiences.* Intelligent ToursThe Panorama Palace uses virtual reality (VR) and 360-degree imaging to reproduce areas of the Forbidden City inaccessible to the public. High-resolution photogrammetry captures details of buildings and objects, which are then stitched into explorable VR environments. Some tours integrate AI-driven narration, adapting content in real time to visitor choices, and offering multilingual interpretation automatically through natural language processing.Impacts on the Cultural SectorThe adoption of these technologies has reshaped multiple dimensions of cultural practice:* Museum management and governancePredictive analytics improves decision-making on crowd control, safety, and logistics. Machine learning models detect anomalies in visitor flow, enabling faster responses to incidents.* Audience engagement and developmentAI-driven personalization generates tailored itineraries by matching visitor profiles (interests, time available, mobility constraints) with curated routes. During the pandemic, VR-based access helped maintain public connection to collections despite closures.* Museology and mediationStorytelling powered by AI—using natural language generation linked to collection databases—allows exhibitions to shift from uniform didactics to adaptive narratives, giving visitors agency in how they explore knowledge.* Communication and community managementPlatforms like Palace Forum integrate recommender systems that highlight discussions or exhibitions aligned with user behavior. Although promising, these remain limited in fostering genuine two-way dialogue between the institution and global audiences.* Exhibition and content productionGenerative AI tools support the reconstruction of lost or damaged objects, while multilingual AI translation engines automatically create interpretation materials in multiple languages. This extends the museum’s reach far beyond its physical walls.Perspectives and IssuesThe trajectory of the National Palace Museum points to future “intelligent museums” where AI agents could act as conversational guides, answering visitor questions in natural language, while collections themselves become interactive through multimodal interfaces. Multilingual mediation could become standard, automatically adapting to visitor needs.Yet these developments also raise significant issues. Reliance on AI-driven narratives risks distorting cultural authenticity if not carefully supervised by curators. Dependence on proprietary technologies raises questions of sovereignty and sustainability. Ethical concerns include privacy risks from biometric data collection, algorithmic bias in recommendations, and the opacity of machine-generated interpretations.ConclusionThe National Palace Museum demonstrates how artificial intelligence can transform a traditional institution into a hybrid cultural platform that is more accessible, participatory, and globally connected. The case illustrates both the opportunities—expanded audience engagement, richer mediation, and more efficient management—and the challenges, from ethical safeguards to professional authority. For cultural professionals, it raises fundamental questions about how curatorial expertise, visitor relationships, and the very definition of a museum will evolve in the age of AI. Get full access to MuseumWeek Magazine at museumweek2h1r4.substack.com/subscribe

Since 2020, computer vision has taken a major leap forward: models have become more accurate, and several museums are now integrating them into their practices. The key question is no longer “Does it work?” but rather “How can we adapt it to our specific needs?”1. Recent Use Cases Illustrating Growing Technical Maturity* Enhanced Visual Accessibility — Houston Museum of Natural ScienceThe Houston Museum of Natural Science (HMNS) has launched the ReBokeh application, designed for visually impaired visitors. In real time, it improves contrast, brightness, and zoom, while also integrating AI-generated audio and text descriptions of the exhibits. Museum staff are trained to support visitors using the app, as part of a broader sensory accessibility program.Source: Houston Chronicle* Visual Exploration of Digital Collections — SMKExplore (National Gallery of Denmark)The SMKExplore project relies on an object-detection pipeline applied to digital collections. The application enables intuitive exploration: visitors navigate through artworks starting from objects automatically detected in images, encouraging a more visual and open-ended approach beyond traditional catalog entries.Source: arXiv* Dynamic Optimization of Exhibition SpacesA 2025 study proposes a model combining reinforcement learning, computer vision, and affective computing. It dynamically adapts exhibition layouts in real time according to visitor behavior, crowding, and interactions. Results show an 18.1% increase in spatial fluidity, a 50% rise in visitation, and content tailored to detected emotions.Source: Nature* Artwork Authentication via Computer Vision — Art RecognitionThe Swiss startup Art Recognition employs AI and computer vision to authenticate artworks and identify forgeries. In May 2024, the system successfully detected counterfeit Monets and Renoirs sold on eBay. By November, an auction house accepted a work authenticated exclusively by AI, signaling a turning point in trust and market practices.Source: Wikipedia* Large-Scale Image–Language Reasoning for Exhibition UnderstandingAn international team compiled a massive dataset of 65 million museum images and 200 million question–answer pairs. Using this corpus, they trained vision–language models such as BLIP and LLaVA to assess their ability to interpret exhibition objects in depth, including questions requiring human-level semantic grounding.Source: arXiv2. Synthesis of Advances and Persistent ChallengesRecent developments demonstrate that computer vision is no longer a mere experimental gadget but a credible and operational tool for museums. The projects above illustrate several major directions:* Enhanced accessibility through applications like ReBokeh, directly improving inclusivity for visually impaired audiences.* Visual exploration of collections, shifting away from rigid cataloging systems toward intuitive, object-driven discovery.* Adaptive spatial management, where reinforcement learning and affective computing allow exhibitions to respond dynamically to visitor behavior.* Artwork authentication, offering new layers of trust — and debate — in the art market.* Multimodal reasoning, enabling AI systems to connect visual recognition with semantic understanding and dialogue.Yet, persistent challenges remain. Institutions face disparities in digital literacy among staff, difficulties in enriching and standardizing metadata, and biases embedded in datasets, particularly regarding non-Western heritage. Moreover, these systems still struggle to provide the depth of historical and cultural context that only human expertise can ensure. Finally, adoption within institutions remains cautious, slowed by budgetary limitations and organizational inertia.3. Conclusion and Professional RecommendationsComputer vision is emerging as a strategic pillar of AI in museums. To harness its potential, institutions should:* Identify high-impact use cases such as accessibility, digital mediation, preventive conservation, and visitor flow management.* Build hybrid teams that bring together curators, mediators, technologists, and ethicists to ensure balanced development.* Pool resources through inter-museum partnerships to create shared and interoperable datasets, reducing duplication of effort.* Develop clear impact indicators to measure not only technical efficiency but also cultural, social, and educational value.* Anticipate ethical and legal issues by drafting AI usage charters and addressing questions of data protection, accountability, and cultural diversity.In short, computer vision is consolidating its place as a foundational component of the 21st-century museum. The central question is no longer whether AI can function, but how museums can integrate it strategically and ethically to strengthen their mission of preservation, mediation, and public engagement. Get full access to MuseumWeek Magazine at museumweek2h1r4.substack.com/subscribe

Introduction – About the PodcastWelcome to the series Museum Professions: Working with AI, part of the AI in the Museum rubric by MuseumWeek. Each episode dives into a specific profession inside the museum world and explores how artificial intelligence is transforming daily practices. Today, we step into the shoes of a Cultural Program Manager.The Job and Its ChallengesCultural Program Managers are responsible for designing, implementing, andevaluating programs that engage diverse audiences while aligning with institutional missions. Their operational challenges include measuring the environmental impact of events, optimizing resource allocation for sustainability, and fostering audience awareness regarding ecological issues. These challenges require a nuanced understanding of both programmatic goals and environmental stewardship.How AI Can Help – Practical Solutions with ToolsChallenge 1: Measuring Environmental ImpactThe Problem: Accurately assessing the ecological footprint of museum events is complex, often requiring extensive data collection and analysis. Without precise metrics, it is challenging for cultural institutions to identify areas for improvement and implement effective sustainability measures. This is crucial not only for compliance with regulations but also for meeting audience expectations regarding environmental responsibility.The AI approach: AI can facilitate this process through data analytics and machine learning algorithms that analyze energy consumption, waste generation, and transportation emissions. Tools like predictive analytics and natural language processing can help in understanding patterns and trends in resource usage.Implementation path: A Cultural Program Manager could utilize tools like Google Cloud AI to gather data from various sources, such as energy bills and event attendance records. By inputting this data into the AI platform, the manager can generate reports that highlight the ecological impact of past events. This analysis can inform future planning, leading to more sustainable practices.Risks & limits: There are ethical considerations regarding data privacy and the potential for biased algorithms. Additionally, the cost of implementing advanced AI solutions may be prohibitive for some institutions.Recommended tools: - Google Cloud AI - IBM WatsonChallenge 2: Optimizing Resource AllocationThe Problem: Cultural events often involve significant resource expenditure, including energy, materials, and human labor. Inefficient resource allocation can lead to unnecessary waste and increased environmental impact. Museums must balance operational needs with sustainability goals, making it essential to optimize resource use.The AI approach: AI-driven optimization tools, such as recommender systems and scheduling algorithms, can help Cultural Program Managers allocate resources more effectively. These tools analyze historical data and current needs to suggest optimal configurations for events, minimizing waste and maximizing efficiency.Implementation path: A Cultural Program Manager could employ a tool like OptimoRoute to plan event logistics. By inputting details about the event, including expected attendance and required resources, the AI can recommend the most efficient use of materials and personnel. This ensures that resources are used judiciously, reducing the overall ecological footprint.Risks & limits: The reliance on AI for decision-making can lead to a lack of human oversight, potentially overlooking unique contextual factors. Additionally, there may be a learning curve associated with adopting new technologies.Recommended tools: - OptimoRoute - Resource GuruChallenge 3: Engaging Audiences in SustainabilityThe Problem: Engaging audiences in sustainability initiatives can be challenging, as many may not be aware of the ecological impacts of their participation in museum events. Effective communication strategies are essential to foster a culture of sustainability within the community.The AI approach: AI tools such as chatbots and sentiment analysis can enhance audience engagement by providing personalized information and feedback. These tools can analyze audience interactions and preferences, tailoring communication to resonate with specific demographics.Implementation path: A Cultural Program Manager could implement a chatbot using ManyChat to interact with visitors before and during events. By asking questions about their sustainability interests and providing tailored content, the museum can raise awareness and encourage sustainable practices among attendees.Risks & limits: There is a risk of alienating audiences if AI interactions are perceived as impersonal or intrusive. Additionally, the effectiveness of these tools depends on the quality of the underlying data and algorithms.Recommended tools: - ManyChat - MonkeyLearnLooking Ahead – Tomorrow’s PossibilitiesIn the next 12 to 24 months, we can expect cultural institutions to increasingly integrate AI tools into their operations. This will likely lead to more data-driven decision-making processes, enhancing sustainability efforts while engaging audiences. However, museums must also prioritize governance frameworks to ensure ethical AI use, addressing concerns around bias and data privacy. Opportunities for collaboration with tech companies and academic institutions may also arise, fostering innovation in the sector.ConclusionIn this episode, we explored how AI tools can empower Cultural Program Managers to measure and reduce the ecological footprint of museum events and activities. By leveraging data analytics, optimization tools, and audience engagement strategies, museums can enhance their sustainability efforts while fulfilling their mission.Reflective questions for professionals: 1. How can your institution better integrate AI tools into its sustainability initiatives? 2. What challenges do you foresee in adopting AI for ecological measurement and engagement? 3. How can you ensure that the use of AI aligns with your museum's ethical standards and community values? 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Introduction – About the PodcastWelcome to the series Museum Professions: Working with AI, part of the AI in the Museum rubric by MuseumWeek. Each episode dives into a specific profession inside the museum world and explores how artificial intelligence is transforming daily practices. Today, we step into the shoes of a Curator.In a rapidly evolving cultural landscape, curators face the challenge of creating engaging narratives that resonate with diverse audiences. Leveraging AI can enhance the depth and accessibility of these narratives, allowing curators to analyze vast datasets, identify trends, and personalize visitor experiences. This exploration will delve into practical AI applications that can transform curatorial practices, ensuring that museums remain relevant and impactful.The Job and Its ChallengesCurators are responsible for developing and managing exhibitions, interpreting collections, and engaging audiences through educational programming. Three major operational challenges include:1. Crafting inclusive narratives that reflect diverse perspectives and histories.2. Analyzing visitor engagement data to tailor experiences and programming.3. Integrating new technologies and methodologies into traditional curatorial practices.How AI Can Help – Practical Solutions with ToolsChallenge 1: Crafting Inclusive NarrativesThe problem of creating inclusive narratives is critical as museums strive to represent diverse voices and histories. Curators often face difficulties in sourcing and integrating varied perspectives, which can lead to a lack of representation in exhibitions. This issue is significant as it impacts audience engagement and the museum's relevance in contemporary society.AI can assist in this challenge through Natural Language Processing (NLP) and sentiment analysis, enabling curators to analyze text data from various sources, such as social media, academic articles, and community feedback. By identifying themes and sentiments, curators can better understand the narratives that resonate with different audience segments.To implement this, a curator might begin by collecting data from community surveys and social media platforms. Using tools like MonkeyLearn, they can apply sentiment analysis to gauge public opinion on specific topics. The outputs can inform exhibition themes and ensure that diverse narratives are included. However, risks such as bias in data interpretation and the ethical implications of AI use must be carefully considered.Challenge 2: Analyzing Visitor EngagementUnderstanding visitor engagement is essential for curators to tailor experiences effectively. However, many museums struggle to interpret engagement data meaningfully, which can lead to missed opportunities for connection and learning. This challenge is particularly pressing as museums seek to enhance audience development and retention.AI can provide solutions through data analytics and recommender systems, allowing curators to analyze visitor behavior and preferences. By leveraging these tools, curators can identify trends and personalize recommendations for exhibitions and programs.For practical application, curators can utilize tools like Tableau to visualize visitor data and uncover insights. By integrating this data into their planning processes, curators can create more engaging and relevant experiences. Nonetheless, challenges such as data privacy concerns and the need for ongoing training in data interpretation must be addressed.Challenge 3: Integrating New TechnologiesThe integration of new technologies poses a significant challenge for curators who may be accustomed to traditional practices. As the digital landscape evolves, curators must adapt to new methodologies to remain effective. This challenge is crucial as it affects the museum's ability to innovate and engage with contemporary audiences.AI can facilitate this transition through machine learning and chatbots, which can streamline workflows and enhance visitor interaction. By automating routine tasks, curators can focus on creative aspects of their work while improving visitor engagement through interactive experiences.To implement this, curators can explore tools like ChatGPT for creating interactive chatbots that provide information about exhibitions and collections. By integrating these technologies, museums can foster a more dynamic relationship with visitors. However, risks such as over-reliance on technology and potential job displacement must be carefully managed.Looking Ahead – Tomorrow’s PossibilitiesIn the next 12 to 24 months, museums can expect to see a significant shift in operations as AI becomes more integrated into curatorial practices. Curators will need to develop new skills in data analysis and technology management while ensuring governance frameworks are in place to address ethical concerns. Opportunities for collaboration with tech companies and educational institutions may arise, but constraints related to funding and resource allocation will need to be navigated.ConclusionThis episode highlights the transformative potential of AI in enriching curatorial narratives. By addressing challenges related to inclusivity, visitor engagement, and technology integration, curators can leverage AI to create more meaningful and relevant experiences for diverse audiences.Reflective questions for museum professionals include: - How can we ensure that AI tools are used ethically and inclusively in our curatorial practices? - What strategies can we implement to engage our communities in the narrative development process? - How can we balance the integration of technology with the traditional values of museum curation? 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Introduction – About the PodcastWelcome to the series Museum Professions: Working with AI, part of the AI in the Museum rubric by MuseumWeek. Each episode dives into a specific profession inside the museum world and explores how artificial intelligence is transforming daily practices.Today, we step into the shoes of a museum social media manager, the person in charge of giving the museum a voice in the digital world.The Job and Its ChallengesImagine you are responsible for managing the social media accounts of a museum.Your tasks include announcing a new exhibition, promoting a family workshop, responding to visitor comments, creating content for an international awareness day, and analyzing last week’s engagement metrics.In a large museum, you might coordinate several platforms with a team. In a smaller one, you may be the only person handling everything: strategy, content creation, community management, and analytics.The recurring question is: how can you produce engaging, varied, and consistent content every day, without exhausting your time and energy?How AI Can Help – Practical Solutions with Tools* Adapting the Museum’s Tone with ChatGPT* Problem: AI-generated posts often sound generic.* Solution: Upload your editorial guidelines (tone of voice, examples of past posts, brand values) into ChatGPT or create a custom agent with GPT Builder.* Example: If your museum’s voice is friendly and educational, prompt: “Write this Instagram caption as if you were a museum educator addressing families with children.” The output will differ from a formal institutional tone.* Creating On-Brand Visuals Quickly* Tool: Canva AI generates images aligned with your branding and adapts them automatically to different social formats (Instagram Story, LinkedIn post, Facebook banner).* Example: For a temporary exhibition, upload your logo and brand colors, then ask: “Generate 3 variations of visuals to promote this exhibition to students.”* Transforming Long Events into Short Clips* Tools: Runway ML or Pika Labs help cut long video recordings into short, dynamic clips.* Example: A 45-minute panel can be turned into five 30-second TikTok or YouTube Shorts videos — ideal for grabbing attention.* Spotting Relevant Trends and Hashtags* Tools: BuzzSumo or Exploding Topics combined with Perplexity AI for analysis.* Example: Ask the AI: “Find 3 trending TikTok formats that could be adapted by a history museum this month.”* Scaling Content Production* Tools: Notion AI or HubSpot Content Assistant.* Example: Provide your event calendar and prompt: “Draft posts for each upcoming museum event, in a consistent style, with calls-to-action for ticketing.” This generates a baseline content plan ready for editing.Looking Ahead – Tomorrow’s PossibilitiesIn the near future, we can imagine AI assistants trained specifically on each museum’s archives, collections, and past exhibitions. These tools could continuously generate draft posts in the museum’s established tone of voice and even detect global trends in real time, suggesting: “Your modern art collection could resonate with today’s rising hashtag.”Conclusion – Keeping the Human VoiceAI won’t replace the role of the social media manager. It helps you work faster, diversify your formats, and find inspiration — but it’s still your responsibility to ensure authenticity, relevance, and cultural meaning.Your mission remains to tell the museum’s story with its unique voice. AI is simply a powerful new creative workshop that allows you to focus on what matters most: connecting people with culture. Get full access to MuseumWeek Magazine at museumweek2h1r4.substack.com/subscribe

🎙️ In This Podcast Episode We cover:• Ada Lovelace's Early Life and Education: Born Augusta Ada Byron on December 10, 1815, she was the only legitimate child of poet Lord Byron and Anne Isabella Milbanke. Her mother, Lady Byron, encouraged Lovelace's interest in mathematics and logic to prevent her from developing what she perceived as her father's "insanity". Lovelace was privately educated in mathematics and science by tutors like Mary Somerville and Augustus De Morgan, the latter noting her potential as an "original mathematical investigator". She married William King in 1835, becoming the Countess of Lovelace in 1838. Despite frequent childhood illnesses, she pursued her studies and later contemplated creating a "calculus of the nervous system". In her adult life, she faced significant financial problems due to gambling.• Collaboration with Charles Babbage: Lovelace first met Charles Babbage in 1833 and developed a strong interest in his Analytical Engine, leading to a long working relationship. Babbage was impressed by her intellect and famously called her "The Enchantress of Number".• The Significance of Her "Notes": Between 1842 and 1843, Lovelace translated an article about the Analytical Engine by Luigi Menabrea. She augmented this translation with seven extensive explanatory notes (A to G), which were about three times longer than the original article. These notes were crucial in explaining the complex functionality of the Analytical Engine, a concept not widely grasped by many scientists or the British establishment at the time.• Pioneering Vision of Computing: Lovelace's most revolutionary insight was recognizing that the Analytical Engine had applications beyond mere numerical calculation. She foresaw the machine's capability to manipulate symbols representing entities other than quantity, such as music, if their fundamental relations could be expressed mathematically. This vision anticipated modern general-purpose computing by a century.• The "First Computer Program" Controversy: Her Note G detailed a method for calculating Bernoulli numbers using the Analytical Engine and is often referred to as the world's first published computer program. However, the Analytical Engine was never built, so the program was never tested. While Lovelace is often credited as the first programmer, some historians argue that Charles Babbage had prepared earlier unpublished programs. Others defend Lovelace's contribution, stating her exposition of the machine's abstract operation was more sophisticated and clearer than Babbage's. Note G also famously contains Lovelace's assertion that the Analytical Engine "has no pretensions whatever to originate anything," a dismissal of artificial intelligence that has been widely debated, notably by Alan Turing.• "Poetical Science" Philosophy and Lasting Legacy: Lovelace described her unique approach as "poetical science," believing that intuition and imagination were essential for applying mathematical and scientific concepts effectively and exploring "unseen worlds". Her enduring legacy is widely commemorated today: the computer language "Ada" was named in her honor, Ada Lovelace Day is celebrated annually on the second Tuesday of October to raise the profile of women in STEM, and numerous awards, institutions, and cultural references bear her name. Get full access to MuseumWeek Magazine at museumweek2h1r4.substack.com/subscribe

As museums invest in digital strategies — from publishing online collections to offering virtual tours and educational content — a silent but radical shift is happening: the decline of traditional organic search visibility in favor of AI-generated answers. Major search engines, especially Google, are increasingly pushing AI-generated overviews that summarize information directly on the results page — bypassing clicks to original websites. For cultural institutions, this poses a double challenge: remaining visible in a filtered ecosystem and maintaining direct engagement with audiences.🎙️ In This Podcast EpisodeWe cover:* Why SEO alone is no longer sufficient for digital visibility;* What an “AI-first” approach means for cultural mediation and public outreach;* How to design your next museum website to be findable and quotable by generative AI;* Emerging audience engagement strategies in an age of filtered interaction.🚨 What’s Changing* Your website can be well-built and fully SEO-optimized — and still become invisible, if users get their answers from AI summaries without visiting your site.* The goal is no longer just ranking high on Google, but being quoted and surfaced by AI models themselves.* This shift introduces a new discipline: GEO (Generative Engine Optimization) — the practice of crafting content in formats that AI systems can ingest, understand, and reuse.🏛️ Why Museums Are Especially VulnerableMuseums produce incredibly rich and valuable content — object descriptions, educational resources, blog posts, virtual exhibits. But in an AI-driven digital landscape, value isn’t enough. What matters is semantic clarity, technical structure, and interoperability.If your site lacks structured data (schema.org), modular content design (e.g., a hub-and-spoke model), or fails to use formats readable by AI, it may simply disappear from the digital ecosystem.🛠️ Where to Start: Trusted Technical ResourcesHere is a selection of reliable guides and frameworks to help your institution prepare:🔹 GEO and AI Optimization* OpenAI – Best Practices for Prompting and Structuring ContentOfficial guide to making your content AI-friendly and usable by language models.* Google Search Central – Structured Data IntroductionLearn how to implement structured data for better visibility in AI-enhanced search.* Aleyda Solis – Generative Engine Optimization (GEO) GuideA practical introduction to GEO by a leading international SEO expert.🔹 Museum-Specific Best Practices* W3C – Web Accessibility Guidelines (WCAG 2.2)Ensure that your site remains readable and accessible for both humans and machines.* schema.org/Museum and schema.org/ExhibitionEventAdd semantic markup to help AI understand your institution and events.References* https://museumsandheritage.com/advisor/posts/google-ai-overviews-how-museums-heritage-attractions-can-take-advantage* https://www.semrush.com/blog/semrush-ai-overviews-study/ Get full access to MuseumWeek Magazine at museumweek2h1r4.substack.com/subscribe

What if AI agents could become trusted collaborators in the daily life of museums—not as replacements, but as thoughtful, responsive partners? This episode of AI in the Museum: Connecting Futures offers a deep dive into the practical and conceptual journey of building such agents. Based on real-world experimentation, we explore how cultural professionals can design AI tools that enhance dialogue, support curatorial workflows, and open new possibilities for mediation and public engagement. We examine the types of intelligence these agents can embody, how their behavior is shaped by prompt engineering and ethical design, and what it takes to integrate them meaningfully into institutional practices. Along the way, we ask critical questions: What does it mean for an AI to "understand" heritage? How do we prevent it from reinforcing biases? And what new kinds of relationships between humans and machines might emerge in the museum of tomorrow?🔗 https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdfThe ”AI in the Museum: Connecting Futures” Podcast is a forward-looking series that explores the impact of artificial intelligence on museums by analyzing technological innovation and its effects on cultural professionals. Through real or fictional interviews, the podcast investigates the transformation of museum roles, the evolving expectations of audiences, shifting cultural practices, and even the redefinition of what a museum is in the 21st century. Key topics include cultural institution management, audience engagement, development, museology, communication, community management, production, and mediation. Get full access to MuseumWeek Magazine at museumweek2h1r4.substack.com/subscribe

The Imperial War Museums have launched a groundbreaking project using AI to transcribe, translate, and analyze over 20,000 hours of oral history recordings. These personal accounts of 20th-century conflict are now searchable, summarized, and accessible through a dynamic interface powered by generative AI. In this episode, we explore how this innovation reshapes public access, curatorial workflows, and the role of digital tools in preserving memory. We also imagine future scenarios where museums become conversational archives, answering questions across time and language. Tune in to discover how AI is not just processing the past — it’s redefining how we connect with it.The ”AI in the Museum: Connecting Futures” Podcast is a forward-looking series that explores the impact of artificial intelligence on museums by analyzing technological innovation and its effects on cultural professionals. Through real or fictional interviews, the podcast investigates the transformation of museum roles, the evolving expectations of audiences, shifting cultural practices, and even the redefinition of what a museum is in the 21st century. Key topics include cultural institution management, audience engagement, development, museology, communication, community management, production, and mediation. Get full access to MuseumWeek Magazine at museumweek2h1r4.substack.com/subscribe

Join us as we explore the National Archives Museum's groundbreaking 'The American Story' gallery, a $40 million renovation that introduces the first AI-powered personalized museum experience on the National Mall. Discover how visitors can scan a QR code, select three topics of interest, and have AI systems curate a unique virtual folder of relevant historical documents as they navigate the exhibition. This episode reveals how this state-of-the-art technology is not just presenting history, but empowering each visitor to find their own meaningful connection to our past through National Archives recordsThe ”AI in the Museum: Connecting Futures” Podcast is a forward-looking series that explores the impact of artificial intelligence on museums by analyzing technological innovation and its effects on cultural professionals. Through real or fictional interviews, the podcast investigates the transformation of museum roles, the evolving expectations of audiences, shifting cultural practices, and even the redefinition of what a museum is in the 21st century. Key topics include cultural institution management, audience engagement, development, museology, communication, community management, production, and mediation. Get full access to MuseumWeek Magazine at museumweek2h1r4.substack.com/subscribe