The chip designer Nvidia has said it will build 500bn ( 378bn) worth of artificial intelligence infrastructure in the US over the next four years, in a sign of manufacturers investing in operations on American soil amid Donald Trump's tariffs. The announcement comes after Trump reiterated threats on Sunday to impose imminent tariffs on the semiconductors that Nvidia makes mostly in Taiwan, and after the chipmaker's chief executive, Jensen Huang, dined at the president's Mar-a-Lago resort earlier this month. Nvidia, whose chips have helped drive the huge wave of artificial intelligence (AI) development in recent years, will work with its manufacturing partners to design and build factories so it can create "supercomputers" completely within the US. Production of its popular Blackwell graphics processing unit has already started at Taiwan Semiconductor Manufacturing Company's plant in Phoenix, Arizona, Nvidia said. Construction of new plants is also under way with the manufacturers Foxconn in Houston and Wistron in Dallas. Mass production at both plants is expected to ramp up in the next 12 to 15 months.

Chipmaker Nvidia says it plans to build artificial intelligence servers worth as much as 500bn in the United States over the next four years with help from partners such as TSMC. Nvidia is the latest US tech firm to back a push by President Donald Trump's administration for local manufacturing. Monday's announcement includes the production of its Blackwell AI chips at TSMC's factory in Phoenix, Arizona, and supercomputer manufacturing plants in Texas by Foxconn and Wistron, which are expected to ramp up in 12 to 15 months. "Adding American manufacturing helps us better meet the incredible and growing demand for AI chips and supercomputers, strengthens our supply chain and boosts our resiliency," Nvidia CEO Jensen Huang said. "Manufacturing AI chips and supercomputers in the US will create hundreds of thousands of jobs in the coming decades," Nvidia said in a statement.

The air transportation local share, defined as the proportion of local passengers relative to total passengers, serves as a critical metric reflecting how economic growth, carrier strategies, and market forces jointly influence demand composition. This metric is particularly useful for examining industry structure changes and large-scale disruptive events such as the COVID-19 pandemic. This research offers an in-depth analysis of local share patterns on more than 3900 Origin and Destination (O&D) pairs across the U.S. air transportation system, revealing how economic expansion, the emergence of low-cost carriers (LCCs), and strategic shifts by legacy carriers have collectively elevated local share. To efficiently identify the local share characteristics of thousands of O&Ds and to categorize the O&Ds that have the same behavior, a range of time series clustering methods were used. Evaluation using visualization, performance metrics, and case-based examination highlighted distinct patterns and trends, from magnitude-based stratification to trend-based groupings. The analysis also identified pattern commonalities within O&D pairs, suggesting that macro-level forces (e.g., economic cycles, changing demographics, or disruptions such as COVID-19) can synchronize changes between disparate markets. These insights set the stage for predictive modeling of local share, guiding airline network planning and infrastructure investments. This study combines quantitative analysis with flexible clustering to help stakeholders anticipate market shifts, optimize resource allocation strategies, and strengthen the air transportation system's resilience and competitiveness.

Referral workflow inefficiencies, including misaligned referrals and delays, contribute to suboptimal patient outcomes and higher healthcare costs. In this study, we investigated the possibility of predicting procedural needs based on primary care diagnostic entries, thereby improving referral accuracy, streamlining workflows, and providing better care to patients. A de-identified dataset of 2,086 orthopedic referrals from the University of Texas Health at Tyler was analyzed using machine learning models built on Base General Embeddings (BGE) for semantic extraction. To ensure real-world applicability, noise tolerance experiments were conducted, and oversampling techniques were employed to mitigate class imbalance. The selected optimum and parsimonious embedding model demonstrated high predictive accuracy (ROC-AUC: 0.874, Matthews Correlation Coefficient (MCC): 0.540), effectively distinguishing patients requiring surgical intervention. Dimensionality reduction techniques confirmed the model's ability to capture meaningful clinical relationships. A threshold sensitivity analysis identified an optimal decision threshold (0.30) to balance precision and recall, maximizing referral efficiency. In the predictive modeling analysis, the procedure rate increased from 11.27% to an optimal 60.1%, representing a 433% improvement with significant implications for operational efficiency and healthcare revenue. The results of our study demonstrate that referral optimization can enhance primary and surgical care integration. Through this approach, precise and timely predictions of procedural requirements can be made, thereby minimizing delays, improving surgical planning, and reducing administrative burdens. In addition, the findings highlight the potential of clinical decision support as a scalable solution for improving patient outcomes and the efficiency of the healthcare system.

Geospatial Knowledge Graphs (GeoKGs) model geoentities (e.g., places and natural features) and spatial relationships in an interconnected manner, providing strong knowledge support for geographic applications, including data retrieval, question-answering, and spatial reasoning. However, existing methods for mining and reasoning from GeoKGs, such as popular knowledge graph embedding (KGE) techniques, lack geographic awareness. This study aims to enhance general-purpose KGE by developing new strategies and integrating geometric features of spatial relations, including topology, direction, and distance, to infuse the embedding process with geographic intuition. The new model is tested on downstream link prediction tasks, and the results show that the inclusion of geometric features, particularly topology and direction, improves prediction accuracy for both geoentities and spatial relations. Our research offers new perspectives for integrating spatial concepts and principles into the GeoKG mining process, providing customized GeoAI solutions for geospatial challenges.

On Tuesday, Alphabet's self-driving vehicle developer Waymo said it would begin operating all-day, curbside pickups and drop-offs at Phoenix Sky Harbor International Airport in Arizona. The announcement came with little fanfare--a post on X. But it signals that after years of delay, self-driving vehicles might be (literally) moving in the right direction. The new curbside airport service sends a good signal about Waymo's business, says Mike Ramsey, an automotive analyst with Gartner. "The airport is the primary destination and departure point for any sort of mobility service, whether it's a cab, shuttle bus--or an autonomous robocab," he says.

As it teased earlier, Canon has launched the R5 II, a successor to the powerful but imperfect EOS R5. With a new 45-megapixel backside-illuminated (BSI) stacked sensor, it not only has superior specs for video, shooting speeds and more, but also adds advanced features like eye-controlled AF. The R5 II was launched alongside Canon's new flagship, the EOS R1, which I've covered in a separate post. With the new R5, Canon has mostly dealt with the original's primary problem: overheating while shooting video. To see what's different and try out some of the new features, I spent some time with an R5 II pre-production camera in Phoenix, Arizona. The R5 II's body is largely the same as before, but there are a couple of key changes.

Elon Musk's brain-implant company Neuralink has chosen the Barrow Neurological Institute in Phoenix, Arizona, as the initial study site to test its Telepathy device. The first participant in Neuralink's study, Noah Arbaugh, underwent a successful procedure at the institute in January to get the device implanted. Known as a brain-computer interface, or BCI, the technology is meant to translate brain signals into commands that control a computer or other external device. Neuralink's goal is to enable individuals with paralysis to use a cursor or keyboard with just their thoughts. In March, Arbaugh demonstrated his ability to use the system in a short livestream on the social media platform X.

Cruise will start re-deploying its autonomous vehicles after a major upheaval last year that led to a pause in its operations, the loss of its CEO and the dismissal of a big chunk of its workforce, including several executives. In a blog post on its website, the GM subsidiary said it's resuming its manual driving activities in order to gather road information and create maps for its autonomous vehicles. The first fleet of Cruise vehicles to go out on the road again will be deployed in Phoenix, Arizona, though the company plans to expand to other cities as it continues to "engage with officials and community leaders." If you'll recall, Cruise suspended all its driverless operations a few weeks after an incident in California, wherein one of its robotaxis ran over and dragged a pedestrian who was hurled onto its path after being hit by another vehicle. Both the California DMV and the California Public Utilities Commission revoked its licenses to operate in the state due to that incident and other safety-related issues.

Intel CEO Pat Gelsinger focused his sales pitch for Gaudi 3 on enterprise customers, telling them a "third phase" of AI will mean automating complex enterprise tasks. Chip giant Intel on Tuesday unveiled its latest chip dedicated to artificial intelligence processing, "Gaudi 3," hot on the heels of arch-rival Nvidia unveiling its Blackwell GPU two weeks prior. Unveiled onstage by CEO Pat Gelsinger, during a live-streamed keynote at the company's customer and partner conference, Intel Vision 2024, in Phoenix, Arizona, the focus was placed on Gaudi 3's appeal to enterprises, with an emphasis on goals such as automating enterprise tasks. Also: Nvidia CEO Jensen Huang unveils next-gen'Blackwell' chip family at GTC Gaudi 3 is the third generation of Intel's dedicated chip for performing artificial intelligence training and inference. Intel acquired the chip family when it bought venture-backed startup Habana Labs of Tel Aviv in 2019 for 2 billion.