It's part of Amazon's efforts to reach net-zero carbon across its operations in the coming years. Amazon Japan has started using the country's iconic bullet trains to move packages between facilities across different regions. The company said teaming up with Japan Railway is part of its efforts to cut both delivery times and carbon dioxide emissions. Japan's Shinkansen can reach speeds of up to 200 mph and can cut down travel times, say, from Tokyo to Osaka from around 8 hours to two-and-a-half hours. They also run on electricity delivered by an overhead electrical system. Back in 2019, the company launched an initiative that aims for net zero carbon emissions for deliveries.

In the race against China for AI supremacy, the United States dominates when it comes to access to the most cutting-edge semiconductors. But when it comes to powering the huge data centres that run on AI chips, China holds the clear advantage. A typical data centre can consume as much electricity as 100,000 households, while next-generation "hyperscale" facilities can gobble up as much power as two million homes, according to the International Energy Agency (IEA). China's access to an abundant supply of cheap electricity places it in the ideal position to meet such colossal energy demands. China already generates more than twice as much electricity as the US, a lead that is expected to widen amid an aggressive state-led investment in the country's energy grid.

Utilizing artificial intelligence and robots -- and more specifically humanoids -- is crucial in making up for Japan's labor shortage. This was the dominant talking point at the Humanoids Summit on Thursday when the two-day event kicked off in Tokyo. Hosted by a California-based robotics company of the same name, it is the first time the summit, which was previously held in Silicon Valley and London, is being held in Asia. It is expected to draw 2,000 attendees from 30 countries and 300 companies, according to the organizers. Japan was chosen for its "foundational role in the global robotics ecosystem for decades," said Terence Bennett, executive director of the Bay Area Robotics Association, in his opening remarks.

Realistic AI-created content to require labels during Japan's election campaigns Aisawa Ichiro of the ruling Liberal Democratic Party speaks during a meeting of lawmakers Wednesday held to discuss the use of AI in election campaigns. The ruling and opposition parties agreed Wednesday to require videos and images related to election campaigns made using artificial intelligence to be labeled as "AI-created," as part of efforts to tackle misinformation on social media during campaign periods. The requirement will apply to videos and images that may be mistaken for those not generated by AI, while those that can be clearly identified as made using AI will be excluded. The parties aim to submit a bill that defines the requirement to parliament during its current session, set to end in July, to put the rule in place ahead of unified local elections next spring. The bill will add a provision to the public offices election law to prohibit internet users from harming the fairness of elections by spreading false information about candidates. Whether to impose penalties on offenders remains to be determined.

The US military has carried out new strikes on Iran, targeting a military site in Bandar Abbas, a strategic port city. US Central Command (Centcom) said its forces also shot down four Iranian one-way attack drones that posed a threat around the Strait of Hormuz. The site in Bandar Abbas was struck as it was about to launch a fifth drone, Centcom said. Iranian media reported that explosions were heard to the east of the city. The strikes come amid a fragile ceasefire between the US and Iran, and protracted negotiations to end the three-month war that has choked traffic in the Strait of Hormuz and shot up global energy prices.

Are robots nearing their ChatGPT moment? Last month at Beijing's half marathon, a robot named Lightning beat the human world record by nearly seven minutes. It's the latest in a string of AI-powered milestones that have got people wondering whether robots are about to enter our everyday lives, just as chatbots have. And the country leading the charge is China, where the government has pledged to invest more than £100bn in robotics over the next 20 years. To find out how robots are already entering the workforce, and what needs to happen to get them cleaning our homes and weeding our gardens, Ian Sample hears from the Guardian's senior China correspondent, Amy Hawkins, and from Nathan Lepora, professor of robotics and AI at Bristol University, who researches how robots can achieve human-like dexterity

Iran and U.S. trade airstrikes after Trump dismisses report of Hormuz deal DUBAI/WASHINGTON - Iran's Revolutionary Guard said on Thursday it targeted a U.S. airbase after the U.S. military carried out what a Washington official said were strikes targeting an Iranian drone operation near the Strait of Hormuz, hours after U.S. President Donald Trump rejected a report he was close to a compromise deal with Tehran. The escalation in hostilities highlighted threats to the tenuous ceasefire between the U.S. and Iran that took effect in early April, dampening hopes for a peace deal and sending oil prices surging again. A U.S. official, who requested anonymity to speak candidly about military operations, said the military shot down four Iranian attack drones and struck a ground control station in the port city of Bandar Abbas that was about to launch a fifth drone. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

Causal-discovery algorithms return a directed graph, yet provide no principled means of distinguishing edge directions identified by the data from those assigned without an identifying assumption. Under the standard Markov and faithfulness conditions, the observational distribution identifies only a Markov equivalence class; orientations within that class are not determined by the joint distribution and cannot be recovered from additional samples alone, but require either a functional restriction or an intervention. We introduce a protocol for observational causal discovery on continuous data that attaches to each candidate edge a discrete impossibility certificate: a RESOLVED code records the identifiability theorem under which the direction was committed, while an IMPOSSIBLE code records the failure mode together with the specific question a domain expert must answer to resolve it. The bivariate cascade is extended with five gated identifiability tiers LSNM, IGCI, Stein, MDL, and PEIT that abstain when their precondition test rejects. Two oracle primitives, the meta-hub query and the node-children query, jointly establish an upper bound of $1+K$ expert interactions sufficient to recover any DAG, where $K$ denotes the number of non-leaf vertices. Under an ideal-oracle assumption, the bound is met exactly on the asia, sachs, child, and alarm benchmarks.

Safety defenses for large language models (LLMs) are typically trained and evaluated on single-turn prompts, yet real attacks often unfold as indirect, multi-turn probing. To defend against this more nuanced form of deception, we present a unified pipeline that generates realistic multi-turn deceptive question sets via multi-objective genetic prompt optimization with co-evolving mutation operators. We validate this dataset through a human study, which also revealed that early generations yielded the most convincing deception and practical constraints such as adherence filtering and ordering effects. Using this data, we were able to detect deceptive attempts to access prohibited information using simple, explainable geometric signals in embedding space coupled with a lightweight feed-forward classifier. Three geometric features (angular coverage, distance ratio, and linearity) augmented with pairwise similarity statistics led to a compact predictive model that achieved consistently high recall (0.89) across base, reworded, and truncated (three-turn) scenarios, with test-time F1 ranging from 0.74-0.86. The results support a central hypothesis that multi-turn deceptive intent leaves a stable geometric footprint that enables lightweight, transparent screening without expensive end-to-end training. We further discuss responsible uses, limitations, and paths toward larger, more diverse human-evaluated datasets. The primary contribution to artificial intelligence is the multi-objective evolutionary framework for prompt generation, and the engineering application is the deployment of a lightweight geometric detection system for LLM safety infrastructure.

Generative models, including diffusion models, are increasingly used as foundation models and adapted through sequential fine-tuning, making continual learning an essential problem setting. However, continual learning in such generative models remains poorly understood: after a task change, what aspects of the learned distribution are most easily lost, and what replay samples should be prioritized? We address these questions through the modern Hopfield energy. Recent links between modern Hopfield networks (MHNs) and diffusion models allow analyses in MHNs to be transferred to diffusion models. We introduce intrinsic forgetting as an increase in Hopfield energy after the task change. In tractable settings in an MHN, we prove that high-energy, outlier-like samples undergo a larger energy increase than cluster-like samples, implying that samples located in sharp, isolated basins are more forgettable. We further analyze memory replay and show that replay is particularly effective for high-energy samples, enabling an energy-based selection of replay samples. We validate these predictions in experiments on MHNs and two diffusion models under continual-learning settings: Stable Diffusion and a pixel-space DDPM. In these diffusion models, Hopfield energy tracks reconstruction-based forgetting, and replay experiments reveal energy-dependent mitigation of forgetting that is consistent with the MHN analysis.