Lumiere, on its part, addresses this gap by using a Space-Time U-Net architecture that generates the entire temporal duration of the video at once, through a single pass in the model, leading to more realistic and coherent motion.

“By deploying both spatial and (importantly) temporal down-and up-sampling and leveraging a pre-trained text-to-image diffusion model, our model learns to directly generate a full-frame-rate, low-resolution video by processing it in multiple space-time scales,” the researchers noted in the paper.

The video model was trained on a dataset of 30 million videos, along with their text captions, and is capable of generating 80 frames at 16 fps. The source of this data, however, remains unclear at this stage.

ICYMI: INTRODUCING MORPHEUS-1 The world’s first multi-modal generative ultrasonic transformer designed to induce and stabilize lucid dreams according to Porphetic #AI Available for beta users Spring 2024.

Startup company Prophetic is set to unveil the “Halo” device to induce lucid dreaming, Fortune reports.

A team of researchers at Facebook’s parent company Meta has come up with a new benchmark to gauge the abilities of AI assistants like OpenAI’s large language model GPT-4.

And judging by current standards, OpenAI’s current crop of AI models are all… still pretty stupid.

The team, which includes “AI godfather” and Meta chief scientist Yann LeCun, came up with an exam called GAIA that’s made up of 466 questions that “are conceptually simple for humans yet challenging for most advanced AIs,” per a yet-to-be-peer-reviewed paper.

The tool, Nightshade, introduces subtle alterations into an image to confuse AI models and “poison” their training data.

Artificial intelligence (AI) has become an indispensable component in the analysis of microscopic data. However, while AI models are becoming better and more complex, the computing power and associated energy consumption are also increasing.

Researchers at the Leibniz-Institut für Analytische Wissenschaften (ISAS) and Peking University have therefore created a free compression software that allows scientists to run existing bioimaging AI models faster and with significantly lower energy consumption.

The researchers have presented their user-friendly toolbox, called EfficientBioAI, in an article published in Nature Methods.