AI

Google has officially accelerated Chrome’s release schedule, shifting from a four‑week cycle to updates every two weeks—a significant change the company says is necessary as artificial intelligence transforms both software development and cybersecurity. The new cadence began this week with the rollout of Chrome version 153 across desktop and mobile platforms.

A growing wave of concern inside leading artificial‑intelligence laboratories intensified this week after an Anthropic researcher announced he is leaving the industry, warning that companies are accelerating toward self‑improving AI systems that could slip beyond human control. The departure has become a flashpoint in an already tense debate over how quickly the sector is advancing and whether existing safety practices are sufficient.

There is no denying that AI is adept at making swift decisions. It can wade through thousands of applications, put a flag on a suspicious transaction, put forward product recommendations and even anticipate a customer’s next move. Yet one has to ask the uncomfortable question: What if the AI has learned the wrong patterns? An unfair outcome does not require an intentionally biased model. A system can be steered off course by bad training data, historical precedents, missing information or featur

Google’s AI-generated answers are becoming a central part of the search experience, with Similarweb reporting that AI Overviews now appear in 43% of Google searches, up from 15% a year earlier . This shift reflects a broader transformation in how people interact with search engines: instead of receiving a list of links, users are increasingly staying within Google’s interface, consuming synthesized information drawn from the sites it indexes. Visits to Google’s conversational AI Mode have also

A newly released industry survey from Infragistics found that 57% of technology leaders now identify AI integration as the biggest challenge facing software development organizations, a substantial increase over previous years.

There is something about software bugs that makes them appear at precisely the wrong time. You are in the thick of development and everything is going well. The build is good, the demo was a success, morale is high. But then an accountant hits a button no one put to the test, or a customer tries to upload a file with some odd name, and before you know it the support team is in for a long afternoon. In a way it is like a leaky roof. Spot the problem on a clear day and you can fix it in a jiffy. T

When people think of Ethical AI, what may come to mind is robots asking themselves important questions like, “Humans have no use, but is it right to just get rid of them, or should we just let them kill each other off?” Serious Robot questions aside, there is no denying that AI (Artificial Intelligence) has become the overachiever in the tech world. It is behind the content you read, the products you are sold, fraud detection, medical image analysis and much more. On occasion it will even seem t

Datadog is expanding its AI-powered observability portfolio with new developer-focused tools aimed at helping engineering teams manage increasingly complex AI-driven applications and workflows.

Red Hat announced a major expansion of its developer portfolio this week with new tools focused on agentic AI development, signaling how enterprise software companies are rapidly adapting to the next phase of AI-powered software engineering. The announcement, made during the company’s Red Hat Summit event in Atlanta, introduced Red Hat Desktop and new capabilities within Red Hat Advanced Developer Suite.

AI innovation runs on data, but the industry has developed a bad habit of pretending that its data practices are simpler, cleaner, and more controlled than they really are. Behind the dashboards and governance slogans lies a growing discomfort: the more data organizations collect, the harder it becomes to justify how it’s used, secured, and shared. This tension sits at the heart of modern AI development - and it’s exactly where federated machine learning begins to change the story. 

Everyone has had that moment: a search or recommendation engine suggests something so oddly specific that it feels either magical or mildly unsettling. “How did it know I wanted that?” quickly becomes, “Should I be concerned that it knows that?” That tension - between delight and skepticism - is exactly where explainable AI enters the conversation.

OpenAI and ServiceNow have announced a strategic partnership aimed at embedding advanced AI agents directly into enterprise software development workflows. The collaboration is designed to help organizations automate complex tasks, accelerate development cycles, and improve operational efficiency across IT and business teams.