This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . Toyota's CUE7 robot shoots hoops using AI You don't need an SSN to open a credit card: Scammers know that Mexico's climate supercomputer could change forecasting Michael Easter and Gary Brecka discuss the'choice' to live to be 100 'CyberGuy' warns of creepy privacy clauses in smart devices Brian Oliver of Gainesville, Florida, spoke with Kurt CyberGuy Knutsson about losing money to scammers claiming to be with PayPal. NEW You can now listen to Fox News articles! Brian Oliver is retired, sharp and financially savvy enough to have a stock-and-bond portfolio worth hundreds of thousands of dollars. He is not the type of person you picture getting scammed.

After OpenAI's Instant Checkout feature fell short, Walmart is instead embedding its Sparky chatbot directly into ChatGPT and Google Gemini. Since November, Walmart has let some ChatGPT users order a limited selection of products without ever leaving OpenAI's chatbot interface. Sales have been disappointing, a Walmart executive vice president exclusively tells WIRED. The results suggest that a future where chatbots and AI agents take over ecommerce is still a way off, if it ever materializes. Last year, OpenAI made a bet that it could boost revenue by charging a commission on purchases made through ChatGPT.

Most of the account takeover scams reported last year were for mobiles, online shopping and credit cards, Cifas said. Most of the account takeover scams reported last year were for mobiles, online shopping and credit cards, Cifas said. Criminals are increasingly exploiting AI technology to take over people's mobile, banking and online shopping accounts, the UK's leading anti-fraud body has warned. Last year, a record number of scams were reported to the national fraud database, fuelled by AI, which allows for large-scale deception on "industrialised" levels, according to Cifas, the fraud prevention organisation. Its report showed 444,000 cases of fraud were reported by its members last year - a 6% increase on 2024.

We introduce ECSEL, an explainable classification method that learns formal expressions in the form of signomial equations, motivated by the observation that many symbolic regression benchmarks admit compact signomial structure. ECSEL directly constructs a structural, closed-form expression that serves as both a classifier and an explanation. On standard symbolic regression benchmarks, our method recovers a larger fraction of target equations than competing state-of-the-art approaches while requiring substantially less computation. Leveraging this efficiency, ECSEL achieves classification accuracy competitive with established machine learning models without sacrificing interpretability. Further, we show that ECSEL satisfies some desirable properties regarding global feature behavior, decision-boundary analysis, and local feature attributions. Experiments on benchmark datasets and two real-world case studies i.e., e-commerce and fraud detection, demonstrate that the learned equations expose dataset biases, support counterfactual reasoning, and yield actionable insights.

Recommender systems have become ubiquitous across a wide range of fields, such as ecommerce, media consumption (including movies, books, music, news, etc.), social networks, finance, and many others, due to their effectiveness in identifying relevant items or content among numerous choices [1, 2]. Traditionally, recommender systems, largely based on collaborative filtering techniques, have focused on recommending individual (or "atomic") items, such as movies or books, by understanding users' preferences for these individual items. However, in certain application domains, recommending "composite" items (i.e., combinations of atomic items) represents a very important capability. For illustration, consider a clothing/fashion recommender system, where we want to recommend "outfits" - combinations of tops (t-shirts, shirts, sweaters) and bottoms (pants, skirts, shorts) - to users. In such a case, multiple fashion items in a recommended outfit ideally have to match both functionally and stylistically, which may require domain expertise (e.g., on things like style compatibility) beyond individual preferences. Another key challenge for such recommender systems is that a given user's personal preference for a composite item may not directly translate to the user's personal preferences for the underlying atomic items and vice versa.

Credit risk models are a critical decision-support tool for financial institutions, yet tightening data-protection rules (e.g., GDPR, CCPA) increasingly prohibit cross-border sharing of borrower data, even as these models benefit from cross-institution learning. Traditional default prediction suffers from two limitations: binary classification ignores default timing, treating early defaulters (high loss) equivalently to late defaulters (low loss), and centralized training violates emerging regulatory constraints. We propose a Federated Survival Learning framework with Bayesian Differential Privacy (FSL-BDP) that models time-to-default trajectories without centralizing sensitive data. The framework provides Bayesian (data-dependent) differential privacy (DP) guarantees while enabling institutions to jointly learn risk dynamics. Experiments on three real-world credit datasets (LendingClub, SBA, Bondora) show that federation fundamentally alters the relative effectiveness of privacy mechanisms. While classical DP performs better than Bayesian DP in centralized settings, the latter benefits substantially more from federation (+7.0\% vs +1.4\%), achieving near parity of non-private performance and outperforming classical DP in the majority of participating clients. This ranking reversal yields a key decision-support insight: privacy mechanism selection should be evaluated in the target deployment architecture, rather than centralized benchmarks. These findings provide actionable guidance for practitioners designing privacy-preserving decision support systems in regulated, multi-institutional environments.

Google introduces a new'Checkout' feature in Gemini and Search AI Mode, allowing US users to make direct purchases through conversations with AI. PCWorld reports that Google launched the Universal Commerce Protocol alongside major retailers like Shopify, Etsy, Walmart, and Target for seamless integration. Users can complete transactions using Google Pay or PayPal, streamlining online shopping by eliminating the need to navigate to separate websites. Google is launching a "Checkout" feature in its Gemini AI chatbot as well as in Google Search's AI Mode, according to a recent blog post . The feature allows users to purchase products without leaving the chat or search interface. Purchases can be completed with Google Pay or PayPal. At the same time, Google is also unveiling its Universal Commerce Protocol (UCP). This is an open standard that enables different AI agents, payment systems, and shops to work seamlessly together.