NVIDIA unveils Project G-Assist to provides context-aware help for PC games

NVIDIA announced new NVIDIA RTX technology to power AI assistants and digital humans running on new GeForce RTX AI laptops.

At Computex 2024, NVIDIA unveiled Project G-Assist — an RTX-powered AI assistant technology demo that provides context-aware help for PC games and apps. The Project G-Assist tech demo debuted with ARK: Survival Ascended from Studio Wildcard. NVIDIA also introduced the first PC-based NVIDIA NIM inference microservices for the NVIDIA ACE digital human platform.

Project G-Assist, a GeForce AI Assistant
AI assistants are set to transform gaming and in-app experiences — from offering gaming strategies and analyzing multiplayer replays to assisting with complex creative workflows. Project G-Assist is a glimpse into this future.

PC games offer vast universes to explore and intricate mechanics to master, which are challenging and time-consuming feats even for the most dedicated gamers. Project G-Assist aims to put game knowledge at players’ fingertips using generative AI.

Project G-Assist takes voice or text inputs from the player, along with contextual information from the game screen, and runs the data through AI vision models. These models enhance the contextual awareness and app-specific understanding of a large language model (LLM) linked to a game knowledge database, and then generate a tailored response delivered as text or speech.

NVIDIA partnered with Studio Wildcard to demo the technology with ARK: Survival Ascended. Project G-Assist can help answer questions about creatures, items, lore, objectives, difficult bosses and more. Because Project G-Assist is context-aware, it personalizes its responses to the player’s game session.

In addition, Project G-Assist can configure the player’s gaming system for optimal performance and efficiency. It can provide insights into performance metrics, optimize graphics settings depending on the user’s hardware, apply a safe overclock and even intelligently reduce power consumption while maintaining a performance target.

 

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