Our study of 22 mitigation techniques and five baselines reveals up to 12.6% fairness variance across identical training runs with identical seeds.

Why physical AI is becoming manufacturing's next advantage From simulation driven development to real world execution, Microsoft and NVIDIA are helping manufacturers leverage AI to cross the industrial frontier with confidence. For decades, manufacturers have pursued automation to drive efficiency, reduce costs, and stabilize operations. That approach delivered meaningful gains, but it is no longer enough. Today's manufacturing leaders face a different challenge: how to grow amid labor constraints, rising complexity, and increasing pressure to innovate faster without sacrificing safety, quality, or trust. The next phase of transformation will not be defined by isolated AI tools or individual robots, but by intelligence that can operate reliably in the physical world . This is where physical AI--intelligence that can sense, reason, and act in the real world--marks a decisive shift.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we explore multi-agent systems and collective decision-making, dive into neurosymbolic Markov models, and find out how robots can acquire skills through interactions with the physical world. What if AI were designed not only to optimize choices for individuals, but to help groups reach decisions together? AIhub Ambassador Liliane-Caroline Demers interviewed Kate Larson whose research explores how AI can support collective decision-making. She reflected on what drew her into the field, why she sees AI playing a role in consensus and democratic processes, and why she believes multi-agent systems deserve more attention.

What happens when you create a social media platform that only AI bots can post to? The answer, it turns out, is both entertaining and concerning. Moltbook is exactly that - a platform where artificial intelligence agents chat amongst themselves and humans can only watch from the sidelines. When ChatGPT gets the result, it treats it just like you had entered it yourself, and uses the result of the program to generate another response. It performs this process over and over again until the AI is satisfied that the task is complete.

The company hopes to use its Bee chatbot to connect compatible users without the need for swipes. Bumble is testing an AI dating assistant called Bee that it hopes will get users on dates without them having to swipe through profiles, writes . The company announced the AI assistant during its fourth quarter earnings, and intends to use the AI in a new experience it calls Dates. When a user opts in to Bumble's Dates feature, Bee performs an onboarding chat where it learns about the users' values, relationship goals, communications style, lifestyle and dating intentions, and then attempts to find other users who share some or all of those traits. Once Bee finds someone compatible, both users are notified in the app that they could be a great match, and receive a summary generated by Bee explaining why.

The rogue AI agents appeared to act together to smuggle sensitive information out of supposedly secure cyber-systems. The rogue AI agents appeared to act together to smuggle sensitive information out of supposedly secure cyber-systems. 'Exploit every vulnerability': rogue AI agents published passwords and overrode anti-virus software Exclusive: Lab tests discover'new form of insider risk' with artificial intelligence agents engaging in autonomous, even'aggressive' behaviours Rogue artificial intelligence agents have worked together to smuggle sensitive information out of supposedly secure systems, in the latest sign cyber-defences may be overwhelmed by unforeseen scheming by AIs. With companies increasingly asking AI agents to carry out complex tasks in internal systems, the behaviour has sparked concerns that supposedly helpful technology could pose a serious inside threat. Under tests carried out by Irregular, an AI security lab that works with OpenAI and Anthropic, AIs given a simple task to create LinkedIn posts from material in a company's database dodged conventional anti-hack systems to publish sensitive password information in public without being asked to do so.

Plus: AI fakes of the Iran war are flooding X--and Grok is failing to flag them. Pokémon Go was the world's first augmented-reality megahit. Released in 2016 by Niantic, the AR twist on the juggernaut Pokémon franchise fast became a global phenomenon. "500 million people installed that app in 60 days," says Brian McClendon, CTO at Niantic Spatial, an AI company that Niantic spun out last year. Now Niantic Spatial is using that vast trove of crowdsourced data to build a kind of world model--a buzzy new technology that grounds the smarts of LLMs in real environments. The firm wants to use it to help robots navigate more precisely.

As companies race to adopt agentic AI to spur innovation and gain efficiency, building the right enterprise data infrastructure has become a critical component of success. In the race to adopt and show value from AI, enterprises are moving faster than ever to deploy agentic AI as copilots, assistants, and autonomous task-runners. In late 2025, nearly two-thirds of companies were experimenting with AI agents, while 88% were using AI in at least one business function, up from 78% in 2024, according to McKinsey's annual AI report . Yet, while early pilots often succeed, only one in 10 companies actually scaled their AI agents. One major issue: AI agents are only as effective as the data foundation supporting them. Experts argue that most companies are seeing delays in implementing AI, not because of shortcomings in the models, but because they lack data architectures that deliver business context to be reliably used by humans and agents.

The civilization made the most of its technology, but everything has its limits. Mayan society often relied on cinnabar, a deep red pigment that got its hue from mercury sulfide. Breakthroughs, discoveries, and DIY tips sent six days a week. A trio of ancient reservoirs in present-day Guatemala is revealing both the strength--and limitations--of Mayan water science. While the civilization's purification techniques resulted in comparatively clean drinking sources, archaeologists say the unknowable consequences of a commonly used, deep-red pigment consistently subjected the Indigenous population to toxic mercury poisoning .

Causal discovery across multiple datasets is often constrained by data privacy regulations and cross-site heterogeneity, limiting the use of conventional methods that require a single, centralized dataset. To address these challenges, we introduce fedCI, a federated conditional independence test that rigorously handles heterogeneous datasets with non-identical sets of variables, site-specific effects, and mixed variable types, including continuous, ordinal, binary, and categorical variables. At its core, fedCI uses a federated Iteratively Reweighted Least Squares (IRLS) procedure to estimate the parameters of generalized linear models underlying likelihood-ratio tests for conditional independence. Building on this, we develop fedCI-IOD, a federated extension of the Integration of Overlapping Datasets (IOD) algorithm, that replaces its meta-analysis strategy and enables, for the fist time, federated causal discovery under latent confounding across distributed and heterogeneous datasets. By aggregating evidence federatively, fedCI-IOD not only preserves privacy but also substantially enhances statistical power, achieving performance comparable to fully pooled analyses and mitigating artifacts from low local sample sizes. Our tools are publicly available as the fedCI Python package, a privacy-preserving R implementation of IOD, and a web application for the fedCI-IOD pipeline, providing versatile, user-friendly solutions for federated conditional independence testing and causal discovery.