Generative AI

In the high-stakes world of smart grids, digital substations have become both operational nerve centers and prime targets for cyberattacks. IEC61850-based communication, particularly GOOSE multicast messages, enables faster coordination but also introduces new vulnerabilities — especially for unmanned substations that rely heavily on remote access. Traditional anomaly detection systems (ADSs), while effective in standard IT contexts, falter here: they require continual retraining for each new threat and often struggle with scarce, imbalanced datasets. ...

The AI Pressroom Arrives — Mostly Unannounced When ChatGPT-3.5 launched in late 2022, it didn’t just disrupt classrooms and coding forums — it quietly walked into newsrooms. A recent large-scale study of 40,000+ news articles shows that local and college media outlets, often operating with lean budgets and smaller editorial teams, have embraced generative AI far more than their major-network counterparts. And in many cases, readers have no idea. The research, spanning opinion sections from CNN to The Harvard Crimson, and across formats from print to radio, found a tenfold jump in AI-written local news opinion pieces post-GPT. College newspapers followed closely with an 8.6× increase, while major outlets showed only modest uptake — a testament to stricter editorial controls or more cautious adoption policies. ...

What if every story, no matter where it’s set, ends with a cheerful festival and a return to tradition? That’s not a hypothetical. It’s what happens when you ask OpenAI’s GPT-4o-mini to generate 11,800 stories, one for nearly every nationality on Earth. Researchers Jill Walker Rettberg and Hermann Wigers did just that — and uncovered a startling truth: generative AI doesn’t just reproduce representational bias (like stereotyping a “doctor” as a white man), it also imposes narrative bias — structural sameness beneath a veneer of cultural difference. ...

In the quest for smarter automation, businesses have long leaned on rigid workflow engines and task-centric diagrams. But in an increasingly dynamic, AI-powered world, these static pipelines are starting to show their cracks. A new paper, “An Agentic AI for a New Paradigm in Business Process Development,” proposes a compelling shift: reframe business processes not as sequences of tasks, but as networks of autonomous, goal-driven agents. From Flowcharts to Ecosystems Traditional business process management (BPM) operates like a production line: each step is predefined, and systems pass the baton from one task to the next. This works well for predictable operations but falters in environments requiring adaptability, exception handling, or dynamic goal reconfiguration. ...

What if we stopped asking language models to “be creative”—and instead let them explore creativity the way humans brainstorm: by remixing ideas, nudging boundaries, and iterating through meaningful variations? That’s exactly what Large Language Models as Innovators proposes: a novel framework that leverages the latent embedding space of ideas—not prompts—to drive controlled, domain-agnostic creativity. Rather than relying on handcrafted rules or complex prompting tricks, the authors show how LLMs can generate original and relevant ideas by interpolating between known concepts, evaluating results, and refining outputs over time. ...

When the AI revolution hits your job, will it help or replace you? Microsoft’s new study, analyzing 200,000 real-world conversations between users and Bing Copilot, offers the most grounded answer to date. Rather than speculating what LLMs could do, this research asks what users are actually doing with them — and how often those interactions overlap with real occupational tasks. The key innovation? The authors distinguish between user goals (what users ask AI to help with) and AI actions (what the AI does in response). This split allows them to track when Copilot acts as a coach, co-pilot, or full-on doer of tasks — a nuance missing from many economic forecasts. ...

For over two decades, group recommender systems (GRS) have been a curiosity in academic circles, promising collective decisions through algorithmic aggregation. Yet despite dozens of papers and prototype systems, they’ve failed to find traction in the real world. Netflix doesn’t use them. Spotify doesn’t bother. Most of us still hash out group decisions in a group chat—awkwardly, inefficiently, and without algorithmic help. The authors of a recent perspective paper argue it’s time for a fundamental reorientation: stop building tools that compute what the group should want, and start designing agents that help the group decide. With the rise of generative AI and agentic LLMs, the timing couldn’t be better. ...

In the symphony of innovation, TRIZ has long served as the structured score guiding engineers toward inventive breakthroughs. But what happens when you give the orchestra to a team of AI agents? Enter TRIZ Agents, a bold exploration of how large language model (LLM) agents—armed with tools, prompts, and persona-based roles—can orchestrate a complete innovation cycle using the TRIZ methodology. Cracking the Code of Creativity TRIZ (Theory of Inventive Problem Solving), derived from the study of thousands of patents, offers a time-tested approach to resolving contradictions in engineering design. It formalizes the innovation process through tools like the 40 Inventive Principles and the Contradiction Matrix. However, its structured elegance demands deep domain expertise—something often scarce outside elite R&D centers. ...

In the rapidly evolving landscape of Generative AI (GenAI), we’ve long relied on static benchmarks—standardized datasets and evaluations—to gauge model performance. But what if the very foundation we’re building our trust upon is fundamentally shaky? Static benchmarks often rely on IID (independent and identically distributed) assumptions, where training and test data come from the same statistical distribution. In such a setting, a model achieving high accuracy might simply be interpolating seen patterns rather than truly generalizing. For example, in language modeling, a model might “memorize” dataset-specific templates without capturing transferable reasoning patterns. ...

Training AI agents to navigate complex environments has always faced a fundamental bottleneck: the overwhelming number of possible actions. Traditional reinforcement learning (RL) techniques often suffer from inefficient exploration, especially in sparse-reward or high-dimensional settings. Recent research offers a promising breakthrough. By leveraging Vision-Language Models (VLMs) and structured generation pipelines, agents can now automatically discover affordances—context-specific action possibilities—without exhaustive trial-and-error. This new paradigm enables AI to focus only on relevant actions, dramatically improving sample efficiency and learning speed. ...