Generative AI is going to play a crucial role in building the embodied internet, aka Metaverse. Democratizing the metaverse would imply giving every creator a set of tools to dream up and build their own immersive canvas that they can share with others. In this talk, I will discuss the challenges and opportunities in generative AI for synthesizing 3D objects, creating/editing digital twins, building virtual worlds, and accelerating the synthesis pipeline.

ChatGPT is the ‘sputnik’ moment for AI in US and Europe – it is now obvious how AI will be fundamental. There is an imperative to future-proof industry by investing in the exploration of the impact of next-generation AI technologies. But, it is also fuelling an urgency for enterprises to capitalize on and deploy core AI technologies today. Models continue to evolve but deployment is becoming more and more important. Smart users are comparing the compute costs and data risks of using massive, proprietary LLMs with smaller, open-source models, fine-tuned to their specific needs. They are searching for much more efficient solutions for deployment today and in the future. In this talk we will discuss:
·         Why LLMs like GPT-4 may not be the best solution for many enterprise AI tasks
·         Identifying the most exciting, enterprise-ready AI solutions for deployment today
·         Why your compute platform matters when using generative AI

The evolution of transformers marks a remarkable advancement in comprehending intricate language patterns. These models are progressively growing in size and precision, enabling them to grasp complex linguistic structures more effectively. However, the real challenge lies in efficiently integrating these advanced models into production systems without incurring exorbitant costs. Striking a balance between superior performance and streamlined resource usage during both training and runtime is crucial for making these large language models (LLMs) viable for deployment in practical production environments.