Making AI systems more transparent and trustworthy: an interview with Ximing Wen

The latest interview in our series with the AAAI/SIGAI Doctoral Consortium participants features Thi Kieu Khanh Ho who is studying time-series anomaly detection. We found out more about her research, and what inspired her to study AI, and what she plans to work on next. Tell us a bit about your PhD — where are […]

Building an agent in an afternoon is now within reach of almost anyone in the enterprise with a credit card. The tools are accessible, the deployments are easy. The hard part is delivering the intended results. Gartner predicts that more than 40% of agentic AI projects will be canceled by 2027, and the EU AI Act Article 14 requirements for human oversight for high-risk AI systems take effect on August 2, 2026. The deciding factor for whether agentic AI reaches production isn’t the model, the framework, or the use case. It’s the infrastructure beneath the agent: the part the people building agents have never had to think about. Organizations are racing to deploy agentic AI to stay competitive, which means pressure-testing is often overlooked. Every agent project should be scrutinized by three executives asking three different sets of questions. The CISO asks whether we are exposed. The CFO asks whether we are overspending. The chief AI officer asks whether we are getting value. As a pr

The move highlights the evolving ties between the US government and leading AI companies as regulators and developers work together to determine how access to highly capable AI systems should be managed.

Nadella's vision emphasizes building proprietary AI systems to ensure sustainable competitive advantage, avoiding reliance on dominant models. The post Microsoft CEO Satya Nadella outlines AI’s future in proprietary learning loops appeared first on Crypto Briefing.

The lawsuit underscores the urgent need for robust mental health safeguards in AI systems, potentially influencing future regulatory frameworks. The post Bipolar man sues OpenAI and Sam Altman over ChatGPT’s role in suicide attempt appeared first on Crypto Briefing.

As organizations rush to move AI into production, they’re finding that the tools they rely on to monitor traditional software don’t translate cleanly to AI systems. The reason is fundamental: AI doesn’t fail as software does. It doesn’t throw clean error codes or follow predictable execution paths. It drifts, hallucinates, and degrades in ways that are often subtle, intermittent, and hard to reproduce. The result is a growing gap between what teams think observability should provide and what current tools actually deliver. The uncomfortable truth? The AI observability tools we have today are built for yesterday’s problems. To understand where the industry is headed, we need to look at where it is today and why that’s not enough. AI observability today: The era of evals Today’s AI observability landscape is dominated by one concept: evaluation. Most tools focus on scoring model outputs after the fact. They rely on test datasets, human graders, or, increasingly, “LLM-as-a-judge” approach

The release of the powerful models shows that enterprises need to be open to different AI systems and consider governance as part of choosing models.

Insider Brief Verkada announced Nvidia has made an investment in the company and is working with it on AI systems to bring its physical security platform to more devices. Verkada said the goal is to bring more context-aware AI into the built environment, where cameras and sensors can help organizations manage safety, security and operations […]