Despite rapid generation of functional code, LLMs are introducing critical, compounding security flaws, posing serious risks for developers.

There are three critical areas where companies most often go wrong: data preparation and training, choosing tools and specialists and timing and planning.

Carey Business School experts Ritu Agarwal and Rick Smith share insights ahead of the latest installment of the Hopkins Forum, a conversation about AI and labor on Feb. 25 ...

With OpenAI's latest updates to its Responses API — the application programming interface that allows developers on OpenAI's platform to access multiple agentic tools like web search and file search ...

AI isn’t killing tech jobs — it’s changing them, favoring pros who pair data and cloud savvy with curiosity, empathy and ...

In the Chicago Urban Heritage Project, College students are turning century-old insurance atlases into interactive digital ...

MiniMax M2.5 delivers elite coding performance and agentic capabilities at a fraction of the cost. Explore the architecture, ...

Security researchers detected artificial intelligence-generated malware exploiting the React2Shell vulnerability, allowing ...

Adversaries weaponized recruitment fraud to steal cloud credentials, pivot through IAM misconfigurations, and reach AI infrastructure in eight minutes.

DuckDuckGo is offering its own voice AI chat feature built using OpenAI models, all for free, and with no data tracking at ...

A marriage of formal methods and LLMs seeks to harness the strengths of both.

OpenAI’s revenue is rising fast, but so are its costs. Here’s what the company’s economics reveal about the future of AI profitability.