Lifeboat Foundation

Current artificial intelligence models utilize billions of trainable parameters to achieve challenging tasks. However, this large number of parameters comes with a hefty cost. Training and deploying these huge models require immense memory space and computing capability that can only be provided by hangar-sized data centers in processes that consume energy equivalent to the electricity needs of midsized cities.

The research community is presently making efforts to rethink both the related computing hardware and the machine learning algorithms to sustainably keep the development of artificial intelligence at its current pace. Optical implementation of neural network architectures is a promising avenue because of the low power implementation of the connections between the units.

New research reported in Advanced Photonics combines light propagation inside multimode fibers with a small number of digitally programmable parameters and achieves the same performance on image classification tasks with fully digital systems with more than 100 times more programmable parameters. This computational framework streamlines the memory requirement and reduces the need for energy-intensive digital processes, while achieving the same level of accuracy in a variety of machine learning tasks.

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.

Continue reading “Google shows off Lumiere, a space-time diffusion model for realistic AI videos” »

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.

Advanced proposition

The iCub3 robot avatar system has been designed to facilitate the embodiment of humanoid robots by human operators, encompassing aspects such as locomotion, manipulation, voice, and facial expressions with comprehensive sensory feedback, including visual, auditory, haptic, weight, and touch modalities.

Continue reading “Italian team’s iCub3 avatar tech set to enhance human-robot connection” »

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.

First such collaboration between a university and ChatGPT.

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

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Study in Npj Digital Medicine evaluates COMPOSER, a deep learning model for early sepsis prediction, showing its effectiveness in improving patient care and reducing in-hospital mortality rates.

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.