We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

Lewis Wallis and Dr Samuel Dicken review 2025 developments in ultra-processed foods (UPF) and high fat, sugar and salt (HFSS) ...

Adaptive test is starting to gain traction for high-performance computing and AI chips as test programs that rely on static limits and fixed test sequences reach their practical limits.

From GPT to Claude to Gemini, model names change fast, but use cases matter more. Here's how I choose the best model for the ...

A peer-reviewed paper about Chinese startup DeepSeek's models explains their training approach but not how they work through ...

SLMs are not replacements for large models, but they can be the foundation for a more intelligent architecture.

Artificial intelligence is increasingly used to integrate and analyze multiple types of data formats, such as text, images, ...

Humans and most other animals are known to be strongly driven by expected rewards or adverse consequences. The process of ...

A practical guide to building AI prompt guardrails, with DLP, data labeling, online tokenization, and governance for secure ...

Developed the world's first multimodal brain signal-based model capable of learning without simultaneous EEG and fNIRS measurements. - Self-learning from data of hundreds of individuals... Introducing ...

Autonomous driving systems increasingly rely on data-driven approaches, yet many still struggle with reasoning, handling rare scenarios, and transparently explaining their actions. A new study ...

This valuable study provides solid evidence for deficits in aversive taste learning and taste coding in a mouse model of autism spectrum disorders. Specifically, the authors found that Shank3 knockout ...