See 10 good vs bad ChatGPT prompts for 2026, with examples showing how context, roles, constraints, and format produce useful answers.

Print Join the Discussion View in the ACM Digital Library The mathematical reasoning performed by LLMs is fundamentally different from the rule-based symbolic methods in traditional formal reasoning.

In some ways, data and its quality can seem strange to people used to assessing the quality of software. There’s often no observable behaviour to check and little in the way of structure to help you ...

India is being targeted by multiple espionage campaigns delivered by the Pakistan-attributed Transparent Tribe (aka APT36).

Israel’s strong presence at the Singapore Airshow highlights a shift toward Asian defense markets as demand for battle‑tested ...

New research from the Oxford Internet Institute (OII) and the University of Kentucky reveals that global biases are reproduced and amplified by LLMs.

Witness nature's magic! We reveal the stunning colors & unique patterns of baby pythons, ensuring each hatchling is healthy & ...

That helpful “Summarize with AI” button? It might be secretly manipulating what your AI recommends. Microsoft security researchers have discovered a growing trend of AI memory poisoning attacks used ...

Apophenia is the mind’s tendency to find meaning in randomness. It shapes creativity, emotion, and misunderstanding, ...

An efficient neural screening approach rapidly identifies circuit modules governing distinct behavioral transitions in response to pathogen exposure.

Running the same 12 prompts 100 times shows how inconsistent ChatGPT brand recommendations are – and why AI visibility tracking falls short.

Fintech is booming because people want quicker, simpler ways to handle their money. Technology keeps getting better, and ...