Lifeboat Foundation

One of the significant challenges in AI research is the computational inefficiency in processing visual tokens in Vision Transformer (ViT) and Video Vision Transformer (ViViT) models. These models process all tokens with equal emphasis, overlooking the inherent redundancy in visual data, which results in high computational costs. Addressing this challenge is crucial for the deployment of AI models in real-world applications where computational resources are limited and real-time processing is essential.

Current methods like ViTs and Mixture of Experts (MoEs) models have been effective in processing large-scale visual data but come with significant limitations. ViTs treat all tokens equally, leading to unnecessary computations. MoEs improve scalability by conditionally activating parts of the network, thus maintaining inference-time costs. However, they introduce a larger parameter footprint and do not reduce computational costs without skipping tokens entirely. Additionally, these models often use experts with uniform computational capacities, limiting their ability to dynamically allocate resources based on token importance.

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In the next 20 years, AI will eat software.

“We made it possible for the computer to write software by itself.”

—NVIDIA CEO Jensen Huang on the future of AI

In 2021, a team led by MIT physicists reported creating a new ultrathin ferroelectric material, or one where positive and negative charges separate into different layers. At the time, they noted the material’s potential for applications in computer memory and much more. Now the same core team and colleagues—including two from the lab next door—have built a transistor with that material and shown that its properties are so useful that it could change the world of electronics.

Although the team’s results are based on a single transistor in the lab, “in several aspects its properties already meet or exceed industry standards” for the ferroelectric transistors produced today, says Pablo Jarillo-Herrero, the Cecil and Ida Green Professor of Physics, who led the work with professor of physics Raymond Ashoori. Both are also affiliated with the Materials Research Laboratory.

“In my lab we primarily do fundamental physics. This is one of the first, and perhaps most dramatic, examples of how very basic science has led to something that could have a major impact on applications,” Jarillo-Herrero says.

A research team led by engineers at the University of Virginia School of Engineering and Applied Science is the first to explore how an emerging plant-based material, cellulose nanofibrils, could amplify the benefits of 3D-printed concrete technology.

“The improvements we saw on both printability and mechanical measures suggest that incorporating cellulose nanofibrils in commercial printable materials could lead to more resilient and eco-friendly construction practices sooner rather than later,” said Osman E. Ozbulut, a professor in the Department of Civil and Environmental Engineering.

His team’s findings will be published in the September 2024 issue of Cement and Concrete Composites.

Here they come… 🦾🤖

And this shows one of the many ways in which the Economic Singularity is rushing at us. The 🦾🤖 Bots are coming soon to a job near you.

NVIDIA unveiled a suite of services, models, and computing platforms designed to accelerate the development of humanoid robots globally. Key highlights include:

Continue reading “NVIDIA Accelerating the Future of AI & Humanoid Robots” »

Tracing the “chemical roots of consciousness,” he ends up affirming panpsychism, but insisting that nature is a “technologist.” Someone must do the thinking.

Centenarians share their best advice for living to 100 years old.

Equitable and accessible education in life sciences, bioengineering, and synthetic biology is crucial for training the next generation of scientists. Here the authors present the CRISPRkit, a cost-effective educational tool that enables high school students to perform CRISPR experiments affordably and safely without prior experience, using smartphone-based quantification and an automated algorithm for data analysis.