Capturing human mobility is essential for modeling how people interact with and move through physical spaces, reflecting social behavior, access to resources, and dynamic spatial patterns. To support scalable and transferable analysis across diverse geographies and contexts, there is a need for a generalizable foundation model for spatiotemporal data. While foundation models have transformed language and vision, they remain limited in handling the unique challenges posed by the spatial, temporal, and semantic complexity of mobility data. This vision paper advocates for a new class of spatial foundation models that integrate geolocation semantics with human mobility across multiple scales. Central to our vision is a shift from modeling discrete points of interest to understanding places: dynamic, context-rich regions shaped by human behavior and mobility that may comprise many places of interest. We identify key gaps in adaptability, scalability, and multi-granular reasoning, and propose research directions focused on modeling places and enabling efficient learning. Our goal is to guide the development of scalable, context-aware models for next-generation geospatial intelligence. These models unlock powerful applications ranging from personalized place discovery and logistics optimization to urban planning, ultimately enabling smarter and more responsive spatial decision-making.

Alzheimer's Disease (AD) early detection is critical for enabling timely intervention and improving patient outcomes. This paper presents a DMV framework using Llama3-70B and GPT-4o as embedding models to analyze clinical notes and predict a continuous risk score associated with early AD onset. Framing the task as a regression problem, we model the relationship between linguistic features in clinical notes (inputs) and a target variable (data value) that answers specific questions related to AD risk within certain topic categories. By leveraging a multi-faceted feature set that includes geolocation data, we capture additional environmental context potentially linked to AD. Our results demonstrate that the integration of the geolocation information significantly decreases the error of predicting early AD risk scores over prior models by 28.57% (Llama3-70B) and 33.47% (GPT4-o). Our findings suggest that this combined approach can enhance the predictive accuracy of AD risk assessment, supporting early diagnosis and intervention in clinical settings. Additionally, the framework's ability to incorporate geolocation data provides a more comprehensive risk assessment model that could help healthcare providers better understand and address environmental factors contributing to AD development.

Bernie Moreno, a Republican U.S. Senate candidate from Ohio, discusses the GOP's eagerness to retake the Senate in November, the illegal immigration crisis and Nikki Haley's refusal to drop out of the primary race. Republican Ohio Senate candidate Bernie Moreno is blasting the Associated Press after a story published days before the primary election linking him to an adult online dating site, which a former intern has taken credit for creating, was called into question by the dating site's founder. On Friday, a post on X from one of the founders of the online site Adult Friend Finder, who says he wrote "most of the early code," seemingly rejected a key aspect of an Associated Press report days earlier that suggested "geolocation data," which is commonly understood as involving an IP address or GPS, linked the account to the area of a Moreno family home. "I reviewed all the available information and it showed that the account had only a single visit, no activity, no profile photo, consistent with a prank or someone just checking out the site," Andrew Conru, the engineer who founded Adult Friend Finder, wrote on social media. "The AP report seeming to claim that the available data proves the account was created in Florida is inaccurate, as location information is manually entered during the signup (sic) process. In reality, there appears to be no public geolocation data tied to the account."

CMO Today delivers the most important news of the day for media and marketing professionals. Companies like Alphabet Inc.'s Google and Meta Platforms Inc.'s Facebook have long recorded users' web behavior to target them with ads. Retailers such as Walmart Inc. and Kroger Co. can track when, where and how you shop for the benefit of brands that advertise with them. Uber has been building its ad business for several years, though most of its growth to date has come from ads placed on the Uber Eats food-delivery app, said Mark Grether, general manager of Uber Advertising. The ride-hailing ad business could grow far larger, Mr. Grether said, especially when self-driving cars become more common.

DUBLIN, April 26, 2022--(BUSINESS WIRE)--The "Global Artificial Intelligence of Things (AIoT) in Telecommunications Growth Opportunities" report has been added to ResearchAndMarkets.com's offering. This report examines the strategic position of telecommunication service providers (TSPs) in using artificial intelligence (AI) and the Internet of Things (IoT) to offer enterprises Artificial Intelligence of Things (AIoT) solutions. TSPs play a vital role in deploying enterprise AIoT solutions amid the increasing deployment of 5G networks, edge infrastructure capabilities, and location-based data at their disposal. Given their network and connectivity capabilities and AI and services focus, TSPs are in a unique position to monetize AIoT opportunities. They increasingly offer solutions by industry vertical as part of their AIoT focus.

When the Russian warship Moskva sank in the Black Sea south of Ukraine, some 500 crew members were reportedly on board. The Russian state held a big ceremony for the surviving sailors and officers who were on the ship. But, considering Russia's history of being not exactly truthful when it comes to events like this, many people wondered whether these were actual sailors from Moskva. Toler is director of research and training for Bellingcat, the group that specializes in open-source and social media investigations. He used facial recognition software to identify the men in the video through images in Russian social media, and found that most of the men were indeed sailors from Sevastopol, the town the ship was operating out of.

Artificial intelligence has become an intricate part of our everyday lives. We encounter it consciously and subconsciously -- at the grocery store, when we call customer service, and even in our homes and cars. With an increasing reliance on a technology designed to constantly collect our data – one that is programmed to be "smarter" than the human brain – are we leaving ourselves open to significant issues such as data breaches or information misuse in the future? How can we mitigate the potential challenges posed by artificial intelligence in healthcare and other industries? The emergence of artificial intelligence in healthcare has brought about countless opportunities for improved patient care outcomes, machine learning-assisted care, and deep learning technological advancements. Although there is no question that artificial intelligence brings added value to the healthcare industry, we also must pause to evaluate the potential challenges that technology-driven patient care poses to patients, providers and healthcare organizations.

A Chinese database has exposed 42.5 million user records that were mined from a range of popular dating apps. The database was discovered by security researcher Jeremiah Fowler, who said it was not password protected and the majority of the records appeared to be from US users. Worryingly, the data left exposed included users' IP addresses, geolocation data, age and usernames. A Chinese database has exposed 42.5 million user records that were mined from a range of popular dating apps. The database included 42.5 million user records from an array of dating apps.

When BJ's Wholesale Club on Thursday (May 3) said that it would leverage artificial intelligence machine learning in its mobile app, it joined the crowded club of companies boasting machine-learning capabilities while remaining vague on the details. But the 215-store chain -- operating in Connecticut, Delaware, Florida, Georgia, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, North Carolina, South Carolina, Ohio, Pennsylvania, Rhode Island and Virginia -- pledged to use machine learning to boost its CRM shopper profiles and to immediately apply it to change mobile responses. "The new discover feature lets shoppers explore new products and easily swipe right to add to a wishlist or left to dismiss a product," the chain said in one of the shortest news releases that retail has ever seen. "Using machine learning, the discover experience will be personalized to each user based on previous selections they've made through the swipe right or left process." Why do I find this so interesting?

You don't even need to make a purchase or visit a website for data science companies to collect information about you. There are all kinds of public data, from property tax records to company and university information, aggregated through startups such as Enigma while Thasos and Reveal Mobile sell pedestrian geolocation data. "I can show that someone saw that ad [online] and actually went into the store," Reveal Mobile CEO Brian Handly told the audience how his startup makes user profiles to map diverse data about individuals at the Artificial Intelligence & Data Science conference in New York City. "Then using that to enhance the advertising targeting." Reveal Mobile passively collects data from more than 50 million phones per month through local news apps, weather apps, travel apps such as Roadtripper and many more.