This short video from RoboCup gives a flavour of the day's happenings, which also featured the opening ceremony.

'There's this deep mystery of what, actually, is this thing?': the philosopher inside Google DeepMind AI Since 2017, Iason Gabriel has worked at the tech giant, trying to anticipate - and think through - the impact of AI. But as commercial and geopolitical pressures escalate, can ethicists make any difference? In 2017, a 33-year-old political philosopher named Iason Gabriel was told by a friend that he ought to apply for a job at DeepMind, the London-based subsidiary of Google where much of its AI research was concentrated. The suggestion was not an obvious one. Gabriel was a cheerful but intense junior academic with a passion for Vipassana meditation and what his brother calls "enthusiastic" rock climbing. At the University of Oxford, where he was a fellow at St John's College, Gabriel taught courses on political theory and wrote papers on the moral contortions of "yuppie ethics" and the ethical blind spots of effective altruism. When he wasn't there, he did crisis work for the United Nations Development Programme in Sudan and Lebanon. DeepMind, meanwhile, was the world's leading AI research lab. In part, this was because it had the financial and computational backing of Google, which had bought the company in 2014 for $650m. In part, it was because DeepMind had recently shown it could put those resources to stunning use. In Seoul, in 2016, a DeepMind system called AlphaGo defeated Lee Sedol, a South Korean Go champion, in a five-game match. The victory was significant not least because of Go's legendary complexity; the game has more possible configurations than there are atoms in the universe. Thanks to the fuss around AlphaGo, Gabriel was aware of DeepMind.

This year, RoboCup will be held in Incheon, South Korea, from 2-6 July. The event will see teams take part in competitions, training sessions, and a symposium. It's an exciting time for RoboCup, as there have been some updates to the leagues and competition format . Most prominently, the soccer leagues will have a primary focus on humanoid robots. A workshop focused on sharing projects, experiences, and innovations in educational robotics.

South Korea has laid out a sweeping industrial strategy focused on semiconductor chips and artificial intelligence projects as President Lee Jae Myung pledges to cement overwhelming industry leadership with investments of hundreds of billions of dollars over several years. Flanked by the heads of the world's two biggest memory chipmakers, Lee cast the initiative on Monday as a "great leap forward" centred on the "triple axis" of semiconductors, physical AI and data centres. The world's two largest memory chipmakers, Samsung Electronics and SK Hynix, will invest 800 trillion won ($518bn) with suppliers to build two new chip fabrication sites each in South Korea's southwest, Industry Minister Kim Jung-kwan said. Lee said the country's southwestern city of Gwangju and South Jeolla province will also invest 5 trillion to 20 trillion won ($3.2bn to $13bn) in the projects. Kim said a further 81 trillion won ($52.5bn) is expected to be invested for a chip-packaging cluster in the Chungcheong area near Seoul.

South Korea has unveiled plans for about $1tn (£760bn) of investments to build out the country's chip manufacturing and artificial intelligence (AI) capabilities in the coming years. It is part of the country's so-called Three Mega Projects to develop new chip production hubs, data centres and robotics technology. The plan is aimed at rejuvenating the economies of areas outside the capital Seoul, President Lee Jae-myung said on Monday. It comes as regional rivals like Taiwan, China and Japan are investing heavily in chip factories and other technologies as the AI boom pushes up demand for semiconductors. We must secure the core elements of AI faster than any other country, Lee said.

South Korean President Lee Jae Myung delivers a speech on June 25. SEOUL - South Korea is set to unveil three "mega-projects" to fuel its next growth phase, including a new semiconductor hub in the southwest that local media say could attract investments by Samsung and SK spanning hundreds of billions of dollars over several years. The announcement would mark President Lee Jae Myung's boldest push yet to align South Korea's AI and chip ambitions with his pledge to narrow regional disparities and revive economies beyond the Seoul metropolitan area. Lee will preside over the event, framed as a national "great leap" due to be unveiled around 2 p.m., his office said, with ministries covering industry, science, climate and transport set to outline policy support. Samsung Electronics and SK are expected to present investment plans, and their chairmen, Jay Y. Lee and Chey Tae-won, are among business leaders tipped to attend by local media. Representatives of other firms including LG Electronics, HD Hyundai Robotics, Korea Electric Power Corp. and Korea Water Resources Corp. are also attending, Lee's office said.

Conformal Bayes combines Bayesian posterior predictives with conformal calibration to produce prediction sets that are both statistically valid and geometrically efficient. We study conformal Bayes under label shift from a unified perspective, identifying two complementary approaches that restore nominal target-domain coverage through importance-weighted conformal calibration but operate through independent mechanisms. \emph{Post-hoc calibration} tilts the posterior predictive toward the target domain and corrects the conformal threshold via an importance-weighted quantile, leaving the parameter posterior unchanged. \emph{In-training adaptation} tilts the parameter posterior itself to the target domain, producing a corrected predictive whose highest predictive density region serves as the highest predictive density (HPD)-based prediction set under the fitted target predictive; efficiency is model-dependent and does not imply finite-sample conditional optimality. Two controlled experiments isolate the regime-dependence of each strategy: in the low-dimensional, well-estimated regime Strategy~A produces the narrowest valid intervals, while in the high-dimensional, underdetermined regime Strategy~B achieves up to $43\%$ width reduction at unchanged coverage, under the stated source-sampling and label-shift assumptions.

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Current AI advances largely rely on scaling neural models and expanding training datasets to achieve generalization and robustness.

We study the problem of computing the exact median by leveraging side information to minimize costly, exact comparisons. We analyze this problem in two key settings: (1) using predictions from unreliable "weak" oracles, and (2) exploiting known structural information in the form of a partial order. In the classical setting, we introduce a modified LazySelect algorithm that combines weak comparisons with occasional strong comparisons through majority voting. We show that this hybrid strategy has near-linear running time and can achieve high-probability correctness using only sublinear strong comparisons, even when the weak oracle is only slightly better than random guessing. Our theoretical results hold under the persistent comparison model, where resampling will not amplify the probability of correctness. In the partially ordered setting, we generalize the notion of median to directed acyclic graphs (DAGs) and show that the complexity of median selection depends heavily on the DAG's width. We complement our analysis with extensive experiments on synthetic data.