Make to Care è nato nel 2016 proprio dalla collaborazione con Maker Faire Rome che da subito ha accolto con entusiasmo la nostra volontà di raccogliere idee che potessero migliorare il quotidiano di chi con coraggio affronta una qualche forma di disabilità. Idee che nascono fuori dai contesti classici dell’innovazione. Negli anni, insieme, abbiamo raccolto centinaia di progetti e prototipi, abbiamo dato voce a spunti davvero rivoluzionari, intercettando, mappando e alimentando l’ecosistema della Patient-driven-Innovation.
Exhibitors 2022
- EDUCATION
- GAMES
- SCIENCE
- STEAM PUNK
- AEROSPACE
- DRONES
- FABRICATION
- ARTIFICIAL INTELLIGENCE
- ROBOTICS
- VIRTUAL REALITY
- NEW MANUFACTURING
- RECYCLING & UPCYCLING
- STARTUP
- ARTISANS & NEW CRAFT
- FASHION & WEARABLES
- PRODUCT DESIGN
- HOME AUTOMATION
- INTERNET OF THINGS
- WELLNESS & HEALTHCARE
- INDUSTRIAL AUTOMATION
- OPEN SOURCE
- 3D PRINTING
- ART
- ENERGY & SUSTAINABILITY
- YOUNG MAKERS (< 18)
- FOOD & AGRICULTURE
- CIRCULAR ECONOMY
- 3D SCANNING
- KIDS & EDUCATION
- CULTURAL HERITAGE
- BIOLOGY
- MUSIC & SOUND
- HACKS
European Network of AI Excellence Centers
One of the funding objectives of Artificial Intelligence in robotics is the development of agents able to interact with the environment. To pursue this goal, an important step is developing systems able to understand the current status of the surrounding world. In this context, vision is one of the most critical perceptual channels and the research community is actively working to develop agents with increasing visual capabilities. Deep Neural Networks are essential tools in this process. They are crucial to tackle many robotics tasks, such as object recognition and segmentation, ego-motion evaluation, and to exploit the combination of vision and language for scene and command understanding.
We will show the most advanced techniques developed by the Politecnico di Torino and CINI within the Elise Project to define the body of knowledge at the basis of the next robot generation and the algorithms that work as core engines of intelligent agents that are able to learn autonomously about objects, scenes, and the open world.
Ellis Unit - Politecnico di Torino
Tatiana Tommasi is an Associate Professor at Polytechnic University of Turin, Italy, and an affiliated researcher at the Italian Institute of Technology. She pioneered the area of transfer learning in computer vision and has extensive experience in domain adaptation, generalization and multimodal learning with applications for robotics and medical imaging. After receiving her PhD from the cole Polytechnique Fdrale de Lausanne, Switzerland, she worked as a post-doctoral researcher at KU Leuven and at University of North Carolina at Chapel Hill. Before moving to Turin, Tatiana was an assistant professor at Sapienza University of Rome. Tatiana is Ellis Scholar and director of the Ellis Unit in Turin.