From the 1st-5th June, the robots descended on Vienna. The 2026 IEEE International Conference on Robotics & Automation (ICRA) brought together the top minds in robotics for one short week to showcase the latest technologies, form new collaborations, and exchange ideas. Held at the Messe Wien, a stone's throw from the bank of the Danube, ICRA proved to be equal parts technological marvel and thought-provoking discussion. The host venue for ICRA 2026: Messe Wien, also known as VIECON. My week at ICRA began with the 2nd ICRA 2026 Workshop on Robot Ethics: Ethical, Legal and User Perspectives in Robotics & Automation (WOROBET) .

A researcher found that using Anthropic's Claude Opus 4.7, he could break into the website of Front Gate--used by every festival from Lollapalooza to Bonnaroo--and freely issue any ticket he chose. Fears about AI tools capable of autonomous hacking usually involve nightmare scenarios like the theft of nuclear launch codes or zeroed-out bank reserves. Far more plausible, it turns out, is asking AI to gain super-administrator access on a ticketing website and then issuing yourself and all of your friends free VIP backstage passes to Bonnaroo. That was the discovery of security researcher Ian Carroll, who used the AI tool Claude Opus 4.7 in April to discover a technique that allowed him full access to the systems of Front Gate Tickets, which handles ticketing for practically every major US music festival, from Lollapalooza and South by Southwest to Austin City Limits. Carroll found that Front Gate, which like Ticketmaster is a subsidiary of the event company Live Nation Entertainment, had a bug in its website that he--with Claude's help--could exploit to gain access to millions of customer or staff records and freely issue tickets for any event, of any value, to himself or whoever he chose.

The UN panel said its approach to AI was'scientific, not political'. The UN panel said its approach to AI was'scientific, not political'. A new United Nations report warns that the development of artificial intelligence may exacerbate global inequality and proposes a shared framework for how to responsibly develop AI, as adoption and investment into the technology accelerates unevenly across the world. "Access to AI tools alone does not produce equal benefit," the report states. "Countries that rely on foreign models, cloud infrastructure and data pipelines may gain access to AI while losing practical control over its standards, safeguards and local fit."

The Trump Administration Is Lifting Its Export Controls on Anthropic's Mythos and Fable AI Models The White House is easing restrictions on Anthropic's most advanced AI models weeks after ordering the company to suspend access for foreign nationals. The Trump administration is lifting export controls on Anthropic's two most powerful AI models after the company reached a deal with the Commerce Department. The news was communicated in a letter sent by Commerce Secretary Howard Lutnick to Anthropic cofounder Tom Brown viewed by WIRED. The department is lifting restrictions on both the Fable 5 model and the more powerful Mythos 5 model, which had so far been approved for release only to select companies and government agencies. "A license is no longer required for the export, reexport, or in-country transfer, including deemed export or deemed reexport, of the Mythos or Fable models," Lutnick wrote.

Agriculture is ready for AI, but its data isn't Data accuracy, structure, and governance are foundational components required for agricultural AI. Artificial intelligence is transforming what is possible in agriculture, but industry leaders should be wary of investing in AI without first laying the groundwork. The use cases are promising, especially for an industry navigating volatile fertilizer costs, unpredictable weather, and margins that leave little room for error. Research shows AI-enabled predictive models can improve crop yield by 26%, reduce water use by 41%, and cut chemical usage by 33%. However, what AI vendors usually won't tell you is that these solutions are only effective if you have a clean, solid data foundation. However, at Reltio, we have experience in this area, including leading technology strategy at a major agricultural distributor and building a data platform used by enterprises worldwide-we've seen it first hand.

'We're up against forces that have all the money in the world': Erin Brockovich on her battle against AI datacentres In 1993, she squeezed a $333m settlement from a Californian energy company in a scandal over contaminated water. Three decades later, she has a new target in her sights - and it's global When Erin Brockovich woke to find 30 emails from people from the same town, she realised something was going on. People email Brockovich all the time because of what happened in 1993, when she was instrumental in suing Pacific Gas and Electric Company (PG&E) on behalf of residents of the town of Hinkley, California, whose groundwater had been contaminated. The case resulted in a settlement of $333m - then the largest ever payout for a direct-action lawsuit. When she was immortalised by Julia Roberts in the 2000 film Erin Brockovich, she became the hero we didn't know we needed, a modern day Joan of Arc.

After weeks of negotiations, the White House permitted Anthropic to grant access to its most advanced AI model to a select group of US companies and government agencies. The US government has eased the restrictions it imposed on Anthropic's most advanced AI model, Claude Mythos 5, allowing the company to grant access to more than 100 US organizations, including large corporations and government agencies. In a letter sent to Anthropic's cofounder and chief compute officer Tom Brown obtained by WIRED, US Commerce Secretary Howard Lutnick told the AI lab it would permit certain trusted partners to access Mythos because he had "determined that appropriate safeguards are in place." Semafor first reported the existence of the letter. "Anthropic has worked with the U.S. government to address risks associated with the Covered Models. These efforts have yielded significant progress," Lutnick wrote.

It's a stretch to think that the continent can build a top-tier model, but it has an advantage: Donald Trump. Emmanuel Macron, president of France, discussed AI's risks at the G7 Summit. Earlier this month I attended Vivatech, a huge tech conference in Paris. One fear dominated the discussions: the prospect of ending up stuck using American AI, trained on American values. While the US and China are locked in an AI arms race, France and Germany, which consider their engineering talent second to none, feel boxed out.

Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures that actively mediate human opportunity. As algorithms increasingly determine access to economic and social opportunities, it has become widely recognized that these systems are deeply embedded with the structural inequalities and prejudices of their environments. The field of algorithmic fairness emerged in response to the growing recognition that models optimized for predictive accuracy can systematically disadvantage marginalized groups. Early mitigation strategies, however, rested on fragile simplifications that limited their effectiveness in complex sociotechnical environments. This thesis identifies and addresses two fundamental limitations of contemporary fairness paradigms: the reliance on deterministic point estimates for auditing and the treatment of individuals as isolated entities devoid of structural context. First, the diagnosis of algorithmic unfairness has traditionally depended on scalar metrics that fail to capture the nuances of real-world deployment. This deterministic approach ignores the high statistical variance inherent in small, intersectional groups, often leading to false alarms or missed detections of bias. Furthermore, standard auditing struggles with the opacity of black-box models, frequently conflating unjustifiable bias with the influence of legitimate features.

The AI arms race between China and the US has researchers on both sides worried about a "Chernobyl moment." Just over a week ago, I attended a major artificial intelligence conference in Zhongguancun, Beijing's bustling high-tech district. It was packed with fascinating sessions touching on everything from recursive self-improvement--the idea that models can tweak their own code and advance indefinitely--to humanoid robots. And it featured a few legends of computing, including Whitfield Diffie, co-inventor of public-key cryptography, and Andrew Barto, who won the Turing Award with Rich Sutton for his pioneering work on reinforcement learning. But I left with one takeaway above all else: The US and China should put their fierce AI rivalry to the side.