Generative AI companies are dominant on this year’s list of the most promising AI startups, heralding a coming productivity revolution.
In a move that directly challenges Nvidia in the lucrative AI training and inference markets, Intel announced its long-anticipated new Intel Gaudi 3 AI accelerator at its Intel Vision event.
The new accelerator offers significant improvements over the previous generation Gaudi 3 processor, promising to bring new competitiveness to training and inference for LLMs and multimodal models.
Gaudi 3 dramatically increases AI compute capabilities, delivering substantial improvements over Gaudi 2 and competitors, particularly in processing BF16 data types, which are crucial for AI workloads.
Data analytics company Databricks says its mission is to deliver data intelligence to every enterprise by allowing organizations to understand and use their unique data to build their own AI systems. Central to that mission is the ability to use a large language model tailored to the needs of the enterprise.
Databricks addresses the need for open LLMs with the release of DBRX, a new open, general-purpose large language model that sets new benchmarks for performance and efficiency. The announcement continues the recent trend of open large language models adapted for the needs of the enterprise.
The open-source DBRX large language model was developed by Databricks’ Mosaic Research team, which it acquired in June 2023 as part of its MosaicML acquisition.
Citrate synthase from the cyanobacterium Synechococcus elongatus is shown to self-assemble into Sierpiński triangles, a finding that opens up the possibility that other naturally occurring molecular-scale fractals exist.
An international team of researchers led by groups from the Max Planck Institute in Marburg and Phillips University in Marburg has now discovered the first regular molecular fractal in nature. They discovered a microbial enzyme—citrate synthase from a cyanobacterium—that spontaneously assembles into a regular fractal pattern known as the Sierpiński triangle. This is an infinitely repeating series of triangles made up of smaller triangles.
“We stumbled on this structure completely by accident and almost couldn’t believe what we saw when we first took images of it using an electron microscope,” says first author Franziska Sendker.
“The protein makes these beautiful triangles and as the fractal grows, we see these larger and larger triangular voids in the middle of them, which is totally unlike any protein assembly we’ve ever seen before,” she continues.
Notably, while other scientists have observed similar phenomena in their laboratory data, the mechanisms behind these observations remained elusive until now. Allan Johnson and his collaborators have elucidated the underlying processes, highlighting the formation of polarons and their ordering in specific directions as a key factor in reducing the energy penalty to the metallic phase. Driving the phase transition by exciting this disordered state of motion can be achieved with less energy.
Furthermore, the dynamic barrier lowering means that scientists are able to selectively reduce the energy required for the laser driven phase transition without increasing the probability of thermal switching, in contrast to other methods for improving the efficiency.
The results have been published in Nature Physics. The implications of this research extend beyond fundamental science, offering new avenues for precise material control and technological innovation. As the team continues to optimize the method and explore new materials, the potential for transformative advancements in material science and optical control remains high.
A team of experts from the University of Barcelona and the company Sensofar Tech have designed an innovative technology to obtain three-dimensional images of a study sample quickly, accurately and non-invasively. The work has been published in Nature Communications.
