artificial intelligence tagged posts

Spiking neural networks (SNNs) are artificial intelligence (AI) models inspired by how biological neurons communicate with each other. While biological neurons exchange information in the form of electrical impulses, SNNs rely on brief signals known as spikes.

SNNs have proved promising for reducing power consumption, as developers can ensure they do not process information continuously, but rather ...

New work explaining the inner workings of artificial intelligence could provide a way around the threat of AI “model collapse,” potentially averting growing numbers of AI hallucinations in the future.

First coined in 2024, “model collapse” refers to a scenario where an AI model trained on AI-produced data ceases to provide accurate results, instead producing inaccurate “gibberish” because of the poor quality of its training data.

Some have warned that high-quality text data to train systems like Large Language Models (LLMs) is set to run out as early as this year, and so data produced by models themselves has taken a larger training role—inviting the threat of model collapse.

Simple statistical models reveal a fix
Through analysis of a simple yet powerful set of statistical ...

A new method developed by MIT researchers can accelerate a privacy-preserving artificial intelligence training method by about 81%. This advance could enable a wider array of resource-constrained edge devices, like sensors and smartwatches, to deploy more accurate AI models while keeping user data secure.

The MIT researchers boosted the efficiency of a technique known as federated learning, which involves a network of connected devices that work together to train a shared AI model.

In federated learning, the model is broad...

A research team led by Lee Hyun Jun and Noh Hee Yeon from the Division of Nanotechnology at DGIST has succeeded in implementing the world’s first two-terminal-based artificial intelligence (AI) semiconductor that precisely controls hydrogen with electrical signals to enable self-learning and memory. The team’s work appears in Advanced Science.

Whereas modern AI requires the rapid processing of vast amounts of data, the separation of computation and memory in conventional computers results in speed degradation and high power consumption...