The games start June 11 and end with a grand finale in New Jersey on July 19. There are 104 of them. Here's how to watch'em all. The FIFA Men's World Cup is almost here, and this one will be the biggest ever. The tournament is hosted by three countries: Mexico, Canada, and the US.

Since its 2009 genesis block, the Bitcoin network has processed >1.08 billion (B) transactions representing >8.72B BTC, offering rich potential for machine learning (ML); yet, its pseudonymity and obscured flow of funds inherent in its UTxO-based design, have rendered this data largely inaccessible for ML research. Addressing this gap, we present an ML-compatible graph modeling the Bitcoin's economic topology by reconstructing the flow of funds. This temporal, heterogeneous graph encompasses complete transaction history up to block 863000, consisting of >2.4B nodes and >39.72B edges. Additionally, we provide custom sampling methods yielding node and edge feature vectors of sampled communities, tools to load and analyze the Bitcoin graph data within specialized graph databases, and ready-to-use database snapshots. This comprehensive dataset and toolkit empower the ML community to tackle Bitcoin's intricate ecosystem at scale, driving progress in applications such as anomaly detection, address classification, market analysis, and large-scale graph ML benchmarking. Dataset and code available at https://github.com/B1AAB/EBA.

No one can say for sure what's going on in the president's head. His 25 greatest obsessions can get us a little closer. This is the year the first baby boomers--those born in 1946--turn 80, and that cohort includes Donald Trump. We have all recently lived through what it means to have an 80-year-old commander in chief, but at a political moment that's simultaneously more horrific, erratic, and just plain befuddling than anything this country has seen in ages, we wanted to understand the brain of 80-year-old president. Plenty of people are trying to discern whether his recent rants and raves are due to a more serious cognitive decline--we understand the instinct; we've done it too --but we went a different (if related) route. The more we dug into Trump's many fixations, the more we realized that this man still thinks he lives in the 1980s. We also discovered--without too much surprise--that he often seems to fundamentally misunderstand the works he treasures most deeply. These items might not replace a brain map, but they do create a certain holistic view of what animates and splinters Trump's mind. Sometimes, they just help explain his worldview. Other times, they seem to have had real influence on policy and the America that Trump is trying to create. Welcome to Trump Brain, the 25 things that define who the president is--and what he wants. Please enable javascript to fully experience this interactive. When millions of people took to the streets in October to protest Trump's authoritarianism, the president responded by dunking on his critics online. Specifically, he posted an A.I.-generated video of a fighter jet, piloted by himself in a literal crown, dropping human excrement onto the crowds. It was perhaps Trump's most juvenile use of A.I. slop yet--the kind of low-quality, feverish content made possible by artificial intelligence. Trump undoubtedly is the perfect president for the A.I. slop era. In some ways, this is because he's the ideal audience for it: Like many older internet users delighted by the technology, Trump seems to enjoy mindless, cartoonish, childish content. One of the videos he shared depicted him playing soccer with Cristiano Ronaldo in the Oval Office.

Affective brain-computer interfaces (aBCIs) play a crucial role in personalized human-computer interaction and neurofeedback modulation. To develop practical and effective aBCI paradigms and to investigate the spatial-temporal dynamics of brain activity under emotional inducement, portable electroencephalography (EEG) signals have been widely adopted. To further enhance spatial-temporal perception, functional near-infrared spectroscopy (fNIRS) has attracted increasing interest in the aBCI field and has been explored in combination with EEG. However, existing datasets typically provide only static fixation labels, overlooking the dynamic changes in subjects' emotions. Notably, some studies have attempted to collect continuously annotated emotional data, but they have recorded only peripheral physiological signals without directly observing brain activity, limiting insight into underlying neural states under different emotions.

Each year the AAAI recognizes a group of individuals who have made significant, sustained contributions to the field of artificial intelligence by appointing them as Fellows. Over the course of the next few months, we'll be talking to some of the 2026 AAAI Fellows. In this interview, we met with Tanya Berger-Wolf, who was elected as a Fellow . We found out about her latest research developing a foundation model for biology, the insights this model can provide, interesting collaborations over the years, and what the future has in store. Could you start with a quick introduction and tell us about the broad area that you're working in? My area of research is in AI for ecology, biodiversity, and conservation.

There has been increasing research interest in AI/ML for social impact, and correspondingly more publication venues refining review criteria for practice-driven AI/ML research. However, these review guidelines tend to most concretely recognize projects that simultaneously achieve deployment and novel ML methodological innovation. We argue that this introduces incentives for researchers that undermine the sustainability of a broader research ecosystem of social impact, which benefits from projects that make contributions on one front (applied methodological) that may better meet project partner needs. Our position is that researchers and reviewers in machine learning for social impact must simultaneously adopt: 1) a more expansive conception of social impacts beyond deployment and 2) more rigorous evaluations of the impact of deployed systems.

Modern AI systems struggle most in environments where reliability is critical - scenes with smoke, poor visibility, and structural deformation. Each year, tens of thousands of firefighters are injured on duty, often due to breakdowns in situational perception. We introduce Fire360, a benchmark for evaluating perception and reasoning in safety-critical firefighting scenarios. The dataset includes 228 360 videos from professional training sessions under diverse conditions (e.g., low light, thermal distortion), annotated with action segments, object locations, and degradation metadata. Fire360 supports five tasks: Visual Question Answering, Temporal Action Captioning, Object Localization, Safety-Critical Reasoning, and Transformed Object Retrieval (TOR). TOR tests whether models can match pristine exemplars to fire-damaged counterparts in unpaired scenes, evaluating episodic memory under irreversible visual transformations. While human experts achieve 83.5% on TOR, models like GPT-4o lag significantly, exposing failures in reasoning under degradation. By releasing Fire360 and its evaluation suite, we aim to advance models that not only see, but also remember, reason, and act under uncertainty.

Deciphering how visual stimuli are transformed into cortical responses is a fundamental challenge in computational neuroscience. This visual-to-neural mapping is inherently a one-to-many relationship, as identical visual inputs reliably evoke variable hemodynamic responses across trials, contexts, and subjects. However, existing deterministic methods struggle to simultaneously model this biological variability while capturing the underlying functional consistency that encodes stimulus information. To address these limitations, we propose SynBrain, a generative framework that simulates the transformation from visual semantics to neural responses in a probabilistic and biologically interpretable manner. SynBrain introduces two key components: (i) BrainVAE models neural representations as continuous probability distributions via probabilistic learning while maintaining functional consistency through visual semantic constraints; (ii) A Semantic-to-Neural Mapper acts as a semantic transmission pathway, projecting visual semantics into the neural response manifold to facilitate high-fidelity fMRI synthesis. Experimental results demonstrate that SynBrain surpasses state-of-the-art methods in subject-specific visual-to-fMRI encoding performance. Furthermore, SynBrain adapts efficiently to new subjects with few-shot data and synthesizes high-quality fMRI signals that are effective in improving data-limited fMRI-to-image decoding performance. Beyond that, SynBrain reveals functional consistency across trials and subjects, with synthesized signals capturing interpretable patterns shaped by biological neural variability.

Availability attacks, or unlearnable examples, are defensive techniques that allow data owners to modify their datasets in ways that prevent unauthorized machine learning models from learning effectively while maintaining the data's intended functionality. It has led to the release of popular black-box tools (e.g., APIs) for users to upload personal data and receive protected counterparts. In this work, we show that such black-box protections can be substantially compromised if a small set of unprotected in-distribution data is available. Specifically, we propose a novel threat model of protection leakage, where an adversary can (1) easily acquire (unprotected, protected) pairs by querying the black-box protections with a small unprotected dataset; and (2) train a diffusion bridge model to build a mapping between unprotected and protected data. This mapping, termed BridgePure, can effectively remove the protection from any previously unseen data within the same distribution. BridgePure demonstrates superior purification performance on classification and style mimicry tasks, exposing critical vulnerabilities in black-box data protection. We suggest that practitioners implement multi-level countermeasures to mitigate such risks.

Watermarking is widely regarded as a promising defense against the misuse of large language models (LLMs); however, existing methods are fundamentally constrained by their vulnerability to scrubbing and spoofing attacks. This vulnerability stems from an inherent trade-off governed by watermark window size: smaller windows resist scrubbing better but are easier to reverse-engineer, enabling low-cost statistics-based spoofing attacks. This work expands the trade-off boundary by introducing a novel mechanism, equivalent texture keys, where multiple tokens within a watermark window can independently support the detection.