AI based on large language models poses risks to Web3 principles. Enter NeuroSymbolic AI, which offers greater auditability ...

Using a clever solution, researchers find GPT-style models have a fixed memorization capacity of approximately 3.6 bits per parameter.

MLCommons' AI training tests show that the more chips you have, the more critical the network that's between them.

MLCommons' AI training tests show that the more chips you have, the more critical the network that's between them.

Whether you're streaming a show, paying bills online or sending an email, each of these actions relies on computer programs ...

Recent advances in deep learning have led to several studies demonstrating ... To address these issues, a Convolutional Encoder- Decoder with Scale-Recursive Reconstructor (ConvED-SR) is proposed for ...

Large language models (LLMs), when trained on extensive plant genomic data, can accurately predict gene functions and ...

A research team has unveiled a groundbreaking study demonstrating the transformative potential of large language models (LLMs ...

By leveraging the structural parallels between genomic sequences and natural language, these AI-driven models can decode ...

In Machine Translation (MT), one of most important research fields ... In this paper, we implement and parallelize batch learning for a Sequence-to- Sequence (Seq2Seq) model, which is the most basic ...

Humans naturally learn by making connections between sight and sound. For instance, we can watch someone playing the cello ...

Standard transformer architecture consists of three main components - the encoder, the decoder and the attention mechanism. The encoder processes input data ...