Future of AI in NeurIPS
In order to enhance generative AI, robotics, and the natural sciences, NVIDIA researchers are working with academic institutions all around the world. More than a dozen of these projects will be presented at NeurIPS, one of the leading AI conferences in the world.
NeurIPS, to be held in New Orleans from December 10–16, brings together specialists in computer vision, machine learning, generative AI, and other fields. NVIDIA Research is going to showcase a number of advancements, including novel methods for converting text into pictures, photos into 3D avatars, and specialized robots into machines with many talents.
According to Jan Kautz, vice president of learning and perception research at NVIDIA, “NVIDIA Research continues to drive progress across the field including generative AI models that transform text to images or speech, autonomous AI agents that learn new tasks faster, and neural networks that calculate complex physics.” “These projects, which are frequently carried out in conjunction with top academic minds, will help accelerate the development of simulations, virtual worlds, and autonomous machines.”
Imagine This: Enhancing Diffusion Models from Text to Image
The most often used kind of generative AI models to convert text into lifelike graphics are diffusion models. Researchers from NVIDIA and academic institutions have worked together on many initiatives that enhance diffusion models and will be showcased at NeurIPS.
The goal of a work approved for oral presentation is to enhance the comprehension of the relationship between primary entities and modifier words in text prompts via generative AI models. Although current text-to-image models that are asked to depict a red lemon and a yellow tomato might produce images of red lemons and yellow tomatoes instead of yellow tomatoes, the new model examines the syntax of the user’s prompt and fosters a relationship between an entity and its modifiers to produce a visual representation of the prompt that is more accurate.
SceneScape is a novel framework that will be displayed as a poster. It uses diffusion models to generate extended movies of 3D sceneries based on text prompts. The project creates films of art museums, haunted houses, and ice castles (seen above) by combining a text-to-image engine with a depth prediction model to enable the videos retain consistent, believable-looking environments across frames.
NVIDIA Research Products at NeurIPS
A different poster outlines research that enhances the generation of ideas by text-to-image models that are seldom observed in training data. When such pictures are attempted to be generated, the end result is typically a low-quality image that does not precisely match the user’s request. In order to assist the model discover suitable seeds random number sequences that direct the AI to produce images from the designated unusual groups, the new technique makes use of a limited collection of sample photos.
A final poster demonstrates how a text-to-image diffusion model may generate missing pieces and produce a complete 3D representation of an item by using the written description of an incomplete point cloud. This might aid in the completion of point cloud data gathered by depth sensors and lidar scanners for robotics and AI applications including autonomous vehicles. Because objects are scanned from a certain angle for instance, a lidar sensor placed on a car would only scan one side of each structure as the car drove along a street collected imagery is sometimes partial.
Character Development: AI Avatar Development Advances
AI avatars generate and animate virtual characters, generate text, and translate it into voice by combining many generative AI models. Two NVIDIA papers at NeurIPS provide fresh approaches to increasing the productivity of these activities.
A poster outlines a novel technique for creating a 3D head avatar from a single portrait photo while preserving accessory and hairdo characteristics. This model offers high-fidelity 3D reconstruction without further optimization during inference, unlike existing approaches that need several pictures and a laborious optimization procedure. The avatars can be animated using a reference video clip in which a person’s movements and facial emotions are transferred to the avatar, or they can be animated using blendshapes, which are 3D mesh representations used to depict various face expressions.
Another poster by NVIDIA researchers and academic partners uses P-Flow, a generative AI model that can quickly generate high-quality individualized speech in response to a three-second reference cue, to develop zero-shot text-to-speech synthesis. P-Flow has superior pronunciation, human resemblance, and speaker likeness as compared to its more modern, cutting-edge equivalents. Almost instantaneously, the model can translate text to speech using a single NVIDIA A100 Tensor Core GPU.
Advances in Robotics and Reinforcement Learning Research
NVIDIA researchers will present two posters in the domains of robotics and reinforcement learning, showcasing breakthroughs that enhance the generalizability of AI across many workloads and situations.
In the first, a methodology for creating algorithms for reinforcement learning that can adjust to novel tasks is presented, all the while avoiding the classic issues of data inefficiency and gradient bias. The researchers demonstrated how effectively their approach worked on a number of benchmark tasks. It is based on a revolutionary meta-algorithm that can produce a robust version of any meta-reinforcement learning model.
Another addresses the problem of object manipulation in robotics and is written by an NVIDIA researcher and partners from a university. Previous AI models that assist robotic hands in grasping and interacting with things are capable of handling certain forms, but they have trouble with items that are not included in the training set. To help the model more quickly adapt to new designs, the researchers develop a novel framework that assesses how items across various categories are geometrically equivalent. Examples of such things include pot lids with identical handles and drawers.
Boosting Science: AI-Accelerated Physics, Environment, and Medical
Additionally, papers in the natural sciences on physics simulations, climate models, and AI in healthcare will be presented by NVIDIA researchers at NeurIPS.
A group of NVIDIA researchers developed the first deep learning-based computational fluid dynamics method on an industry-standard, large-scale automotive benchmark. The neural operator architecture they proposed combines accuracy and computational efficiency to estimate the pressure field surrounding vehicles, accelerating computational fluid dynamics for large-scale 3D simulations. Compared to another GPU-based solution, the technique reduced the error rate and achieved 100,000x acceleration on a single NVIDIA Tensor Core GPU. Researchers can use the open-source neuraloperator library to integrate the model into their own applications.
ClimSim is a big dataset for physics and machine learning-based climate research that will be given in an oral session at NeurIPS. It was developed by a partnership of climate scientists and machine learning researchers from universities, national labs, research institutes, Allen AI, and NVIDIA. The high-resolution dataset spans the world over several years, and machine learning emulators developed with that data may be used to enhance the realism, accuracy, and precision of already in use operational climate simulators. This can aid scientists in making more accurate forecasts of storms and other dramatic occurrences.
A poster revealing an AI algorithm that offers individualized predictions of how a patient’s dosage of medication would affect them is being presented by NVIDIA Research interns. The researchers examined the model’s predictions of blood coagulation for patients receiving various therapy doses using actual patient data. Additionally, they examined the new algorithm’s predictions regarding the levels of the antibiotic vancomycin in patients who were prescribed the drug, and they discovered that the prediction accuracy was noticeably higher than that of earlier techniques.
