Other projects funded by Veneto Region
Venice School of Management

European Regional Development Fund (FESR) 2021-2027

INDICE
Digital Innovations for Cultural and Creative Enterprises

The mono-network project Digital Innovations for Cultural and Creative Enterprises, acronym: INDICE, proposes to respond to the growing need for Research & Development (R & D) and make accessible and centralise the ICT (Information and Communication Technologies) innovations, in the field of culture and creativity of the territory of the Veneto Region. The INDICE project represents an important step forward in the integration of digital technologies in the different cultural and creative sectors of the Veneto Region. Thanks to the collaboration between RIR companies and the 4 universities that are members of its scientific committee (UniPD, UniVR, UniVE and IUAV), the project realizes under a single file rouge innovation ICT:

• five different solutions and technological innovations applied through Big Data, Artificial Intelligence (AI), the Digital Twin, biometric security technologies and 3D animation. 5 different technological innovations serving cultural and creative enterprises and cultural tourism; creating replicable solutions that are opportunities for new products, services and therefore growth economic and market conditions on our territory and in other national and international contexts;
• a single platform (DAM Digital Asset Management) to make accessible to companies and public and private stakeholders the data and information obtained in the project. The platform will also have the ability to place and then communicate and insert data within the ecosystem, centralising relevant information in a single regional hub, through collaboration with Veneto Region.

SUNRISE
Unified System for Narrative, Representation, Innovation, Style, and Expression in Artistic Manufacturing

Artistic manufacturing is at a crossroads between preserving traditional skills and adopting innovative technologies. On one hand, artisanal tradition is based on the ability to create new forms and models through direct experience and manual skill, as the act of "doing," combined with imagination, facilitates the innovation and realization of creative ideas.
On the other hand, the adoption of new technologies, such as CAD software and other digital design tools, tends to automate production processes. While these tools facilitate the refinement of components and the final shape of an object, relying solely on digital methods can become a trap that prevents the designer from fully understanding the intersection of material and form.
Artificial Intelligence (AI) amplifies these concerns, threatening to automate creative processes and replace the artisan. AI's ability to generate images, conceive designs, and "think" creatively raises questions about the integrity and future of artistic manufacturing.
The SUNRISE project, led by RIR EUTEKNOS, aims to address this challenge by taking a new path and introducing a paradigm that contrasts the concept of augmentation with that of automation. This approach seeks to reconcile the role of the artisan, promoting a competence-enhancing model that amplifies human capabilities through the strategic integration of AI.
SUNRISE is based on the synergistic combination of two distinct but complementary cognitive modes (Micelli, 2011):

• "T" Intelligence (Technological): Capable of processing and analyzing large volumes of information, with distributed, adaptive, and data-driven knowledge.
• "A" Intelligence (Artisanal): Vertical expertise, rooted in practical experience and a deep understanding of traditional materials and techniques.

To this end, SUNRISE intends to develop an AI-based virtual assistant to support companies in the artistic manufacturing sector. The goal is to help them in the stylistic definition of their products and the generation of new design ideas. The project involves the development of a two-level AI model:

1. General Model: An initial AI model is created, trained with data collected during a preliminary research phase. In this phase, the inputs include:
   Detailed images of design items, used to train the model to recognize visual features such as shapes, colors, textures, decorative patterns, etc.
   Texts that describe the products, providing information on aspects like style (e.g., modern, classic, minimalist), inspiration (e.g., natural elements, historical art), materials used, and manufacturing techniques. These descriptions help the model understand the language used to describe stylistic features and correlate terms with images.
   The model built in this way will be able to classify design products into specific stylistic categories, providing a common knowledge base that translates into a sort of "shared vocabulary" for companies in the sector.
2. Company Specialization: Subsequently, the model will be specifically adapted for each company participating in the R&D project using the RAG (Retrieval-Augmented Generation) approach. This method allows the knowledge of generative AI models to be extended using data and specific documents from the companies involved. The model will then be able to provide personalized design suggestions aligned with the company's style and identity, using data from catalogs, websites, and other useful materials provided by the companies themselves.