Earthquake forecasting remains a significant scientific challenge, with current methods falling short of achieving the performance necessary for meaningful societal benefits. Traditional models, primarily based on past seismicity and geomechanical data, struggle to capture the complexity of seismic patterns and often overlook valuable non-seismic precursors such as geophysical, geochemical, and atmospheric anomalies. The integration of such diverse data sources into forecasting models, combined with advancements in AI technologies, offers a promising path forward. AI methods, particularly deep learning, excel at processing complex, large-scale datasets, identifying subtle patterns, and handling multidimensional relationships, making them well-suited for overcoming the limitations of conventional approaches. This review highlights the importance of combining AI with geophysical knowledge to create robust, physics-informed forecasting models. It explores current AI methods, input data types, loss functions, and practical considerations for model development, offering guidance to both geophysicists and AI researchers. While many AI-based studies oversimplify earthquake prediction, neglecting critical features such as data imbalance and spatio-temporal clustering, the integration of specialized geophysical insights into AI models can address these shortcomings. We emphasize the importance of interdisciplinary collaboration, urging geophysicists to experiment with AI architectures thoughtfully and encouraging AI experts to deepen their understanding of seismology. By bridging these disciplines, we can develop more accurate, reliable, and societally impactful earthquake forecasting tools.

Classical point process models, such as the epidemic-type aftershock sequence (ETAS) model, have been widely used for forecasting the event times and locations of earthquakes for decades. Recent advances have led to Neural Point Processes (NPPs), which promise greater flexibility and improvements over classical models. However, the currently-used benchmark dataset for NPPs does not represent an up-to-date challenge in the seismological community since it lacks a key earthquake sequence from the region and improperly splits training and testing data. Furthermore, initial earthquake forecast benchmarking lacks a comparison to state-of-the-art earthquake forecasting models typically used by the seismological community. To address these gaps, we introduce EarthquakeNPP: a collection of benchmark datasets to facilitate testing of NPPs on earthquake data, accompanied by a credible implementation of the ETAS model. The datasets cover a range of small to large target regions within California, dating from 1971 to 2021, and include different methodologies for dataset generation. In a benchmarking experiment, we compare three spatio-temporal NPPs against ETAS and find that none outperform ETAS in either spatial or temporal log-likelihood. These results indicate that current NPP implementations are not yet suitable for practical earthquake forecasting. However, EarthquakeNPP will serve as a platform for collaboration between the seismology and machine learning communities with the goal of improving earthquake predictability.

Stephanie Palermo wasn't interested in living in a "smart home" outfitted with web-connected appliances controlled remotely by phone or computer. She didn't need her fridge to have Wi-Fi or her blinds to close themselves. But when Amazon temporarily discounted the price on its voice-controlled Echo speaker to 99 for Amazon Prime members, there was "a low barrier to entry," and the 28-year-old from Belmont, Calif., was willing to take a risk. She started using the cylindrical device as a hands-free speaker. During board game nights, she'd tell Alexa -- the artificial intelligence that powers the Echo -- to play themed music from Pandora.

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After Elon Musk announced Wednesday that all new Tesla vehicles will be equipped with hardware to transform them into driverless cars as soon as the software and regulations are ready, the company posted this video. There's a guy in the driver's seat as the car wheels through Silicon Valley on its own, "for legal reasons," but his hands stay clear of the wheel. Then, at the 2:30 mark in the video, the man gets out of the car in a Tesla parking lot, and the vehicle heads out to find itself a parking spot, totally on its own. The final presidential debate, earthquake forecasting, Game 4 of the Dodgers vs. Cubs, and Bridge to Nowhere bungee jumping faces opposition. The final presidential debate, earthquake forecasting, Game 4 of the Dodgers vs. Cubs, and Bridge to Nowhere bungee jumping faces opposition.