Have you ever visited a museum and left feeling unfulfilled, wishing you could have learned more in a way that resonated with you personally? Traditional museum experiences often rely on static placards or one-size-fits-all audio guides, which may not cater to the diverse interests and learning styles of all visitors.
This solution aims to revolutionise this experience by introducing an AI-driven interactive guide. Utilising NVIDIA Jetson Orin Dev kit's edge computing capabilities, this guide offers real-time, personalised interaction, adapting to individual learning preferences and engaging visitors in a dynamic and informative way. This not only enhances the educational aspect of museum visits but also promises to make cultural exploration more accessible and appealing to a broader audience, fostering a deeper connection with our heritage and arts.
Let's get started...
1. Setting up the NVIDIA Dev kit1.1 Orin intro and why
1.2 Set up
2. Choosing the LLMsTODO: how to combine vision (to interpret what painting we are looking at, which room we are in for the visit?) and speech interaction (to talk live with chat bot/model)
-> Should those be llamaspeak and LLaVa?
Will we need to use NanoDB to feed training data (relevant content)
https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/local_llm#multimodal-chat
3 TrainingTODO: find resources related to paintings available at the national gallery - maybe focus on 2 rooms (to demonstrate geospatial awareness within museum through vision)
3.1 Identifying and confirming the content
3.2 Training the model?
3.3 How does it know you are a child or an adult to adapt the speech?
4. TestingDoes it behave as expected and
5. Final buildPutting it all together: nice packaging using 3D printing, + final demo
Conclusion and next stepsUseful links:
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