#Artificial Intelligence

DeepMind explores how advanced AI could amplify cyber threats making attacks stealthier, scalable, and autonomous. Research highlights the urgent need to understand and mitigate these risks to ensure AI's cybersecurity benefits outweigh potential harms.

This article explores the question of trust in Large Language Models, drawing a parallel to Hannah Arendt's warning about the consequences of constant lies. It suggests that if LLMs are not reliable, we risk broader societal distrust in information itself.

OpenAI has secured a massive $40bn investment from SoftBank, valuing the company at $300bn. This record-breaking deal aims to fuel the development of artificial super intelligence.

Nvidia plans a massive $500B US AI infrastructure build over 4 years, spurred by looming chip tariffs and Trump's policies. This move signals manufacturing investment in the US amidst trade tensions.

Generative AI and LLMs are reshaping how we work and learn, but they are prone to 'hallucinations.' This article sheds light on this crucial issue in AI.

Dr. Oz, leading Medicare & Medicaid, suggested AI could be superior to frontline doctors at a staff meeting. He also emphasized a 'patriotic duty' for staff to stay healthy, sparking debate about AI's role in healthcare.

GenAI's rise forces both Computer Science and Humanities to confront an 'existential crisis,' questioning their purpose, relevance, and methodologies in this transformative tech era.

Article explores why, contrary to 1960s visions of seamless human-AI teamwork, accepting AI suggestions is not always straightforward and can become harder with deeper thought.

MIT researchers are investigating if Large Language Models (LLMs) like ChatGPT can accelerate and reduce the cost of discovering new medicines and materials. The current process is slow and resource-intensive, and LLMs could offer a faster, more efficient alternative for molecule screening.