With the advent of the age of big data, people can collect rich and diverse data from a wide variety of collection devices, such as those of the Internet of Things. Knowledge hidden in large data is ...

The SAIR database is designed to help scientists train AI models to better predict protein-ligand binding affinities for drug ...

Nebius leverages NVIDIA Blackwell GPUs and strategic partnerships for AI dominance. Discover why it's poised for 55% upside ...

The pharmaceutical industry is on the cusp of an AI-driven revolution. By 2030, AI-powered drug discovery is projected to be a $9.1 billion market, growing at a staggering 29.7% CAGR. AI promises ...

Key Innovations in Arweave 2.9: New Data Activation Algorithm: This innovative algorithm replaces the previous packing method, significantly lowering the computational requirements for honest miners.

A team has developed a new method that facilitates and improves predictions of tabular data, especially for small data sets with fewer than 10,000 data points. The new AI model TabPFN is trained ...

Existing methods for prefix sum computations include tree-based algorithms like Brent-Kung, which optimize the trade-offs between depth and work in the PRAM model. However, these algorithms are ...

The team designed a fully dynamic APSP algorithm in the MPC model with low round complexity that is faster than all the existing static parallel APSP algorithms.

There are different parallel models, such as shared memory, distributed memory, message passing, data parallel, task parallel, and hybrid models. Each model has its own advantages and ...

An AI model is a mathematical representation—implemented as an algorithm, or practice—that generates new data that will (hopefully) resemble a set of data you already have on hand.