AI Technology & Industry Review
In the new paper Shap·E: Generating Conditional 3D Implicit Functions, an OpenAI research team proposes Shap·E, a conditional generative model that leverages implicit neural representations to produce complex and diverse 3D assets.
In the new paper Generative Agents: Interactive Simulacra of Human Behavior, a team from Stanford University and Google Research presents agents that draw on generative models to simulate both individual and emergent group behaviours that are humanlike and based on their changing experiences and environment.
In the new paper Unlocking de Novo Antibody Design With Generative Artificial Intelligence, researchers from Absci Corporation leverage the power of generative artificial intelligence for de novo antibody design in a zero-shot and controllable manner, dramatically reducing time and resource requirements for the task.
In the new paper Muse: Text-To-Image Generation via Masked Generative Transformers, a Google Research team introduces Muse, a transformer-based text-to-image synthesis model that leverages masked image modelling to achieve state-of-the-art performance while being significantly faster than diffusion or autoregressive models.
A research team from Mila, McGill University, Université de Montréal, DeepMind and Microsoft proposes GFlowNet, a novel flow network-based generative method that can turn a given positive reward into a generative policy that samples with a probability proportional to the return.v
It’s known that deep neural network models — mostly discriminative models — are prone to making poor predictions with high confidence when they encounter data distribution scenarios that vary too much from their training data.