Survival analysis has become a standard approach for modelling time to default by time-varying covariates in credit risk. Unlike most existing methods that implicitly assume a stationary data-generating process, in practise, mortgage portfolios are exposed to various forms of data drift caused by changing borrower behaviour, macroeconomic conditions, policy regimes and so on. This study investigates the impact of data drift on survival-based credit risk models and proposes a dynamic joint modelling framework to improve robustness under non-stationary environments. The proposed model integrates a longitudinal behavioural marker derived from balance dynamics with a discrete-time hazard formulation, combined with landmark one-hot encoding and isotonic calibration. Three types of data drift (sudden, incremental and recurring) are simulated and analysed on mortgage loan datasets from Freddie Mac. Experiments and corresponding evidence show that the proposed landmark-based joint model consistently outperforms classical survival models, tree-based drift-adaptive learners and gradient boosting methods in terms of discrimination and calibration across all drift scenarios, which confirms the superiority of our model design.

Generating dense physical fields from sparse measurements is a fundamental question in sampling, signal processing, and many other applications. State-of-the-art methods either use spatial statistics or rely on examples of dense fields in the training phase, which often are not available, and thus rely on synthetic data. Here, we present a reconstruction method that generates dense fields from sparse measurements, without assuming availability of the spatial statistics, nor of examples of the dense fields. This is made possible through the introduction of an automatically differentiable numerical simulator into the training phase of the method. The method is shown to have superior results over statistical and neural network based methods on a set of three standard problems from fluid mechanics.

We prove an exponential separation in sample complexity between Euclidean and hyperbolic representations for learning on hierarchical data under standard Lipschitz regularization. For depth-$R$ hierarchies with branching factor $m$, we first establish a geometric obstruction for Euclidean space: any bounded-radius embedding forces volumetric collapse, mapping exponentially many tree-distant points to nearby locations. This necessitates Lipschitz constants scaling as $\exp(Ω(R))$ to realize even simple hierarchical targets, yielding exponential sample complexity under capacity control. We then show this obstruction vanishes in hyperbolic space: constant-distortion hyperbolic embeddings admit $O(1)$-Lipschitz realizability, enabling learning with $n = O(mR \log m)$ samples. A matching $Ω(mR \log m)$ lower bound via Fano's inequality establishes that hyperbolic representations achieve the information-theoretic optimum. We also show a geometry-independent bottleneck: any rank-$k$ prediction space captures only $O(k)$ canonical hierarchical contrasts.

ICE Is Using Palantir's AI Tools to Sort Through Tips ICE has been using an AI-powered Palantir system to summarize tips sent to its tip line since last spring, according to a newly released Homeland Security document. United States Immigration and Customs Enforcement is leveraging Palantir's generative artificial intelligence tools to sort and summarize immigration enforcement tips from its public submission form, according to an inventory released Wednesday of all use cases the Department of Homeland Security had for AI in 2025. The AI Enhanced ICE Tip Processing service is intended to help ICE investigators "to more quickly identify and action tips" for urgent cases, as well as translate submissions not made in English, according to the inventory. It also provides a "BLUF," defined as a "high-level summary of the tip," produced using at least one large language model. BLUF, or "bottom line up front," is a military term that's also used internally by some Palantir employees.

The AI company shouting about AI's dangers can't quite bring itself to slow down. T hese are not the words you want to hear when it comes to human extinction, but I was hearing them: "Things are moving uncomfortably fast." I was sitting in a conference room with Sam Bowman, a safety researcher at Anthropic. Worth $183 billion at the latest estimate, the AI firm has every incentive to speed things up, ship more products, and develop more advanced chatbots to stay competitive with the likes of OpenAI, Google, and the industry's other giants. But Anthropic is at odds with itself--thinking deeply, even anxiously, about seemingly every decision. Anthropic has positioned itself as the AI industry's superego: the firm that speaks with the most authority about the big questions surrounding the technology, while rival companies develop advertisements and affiliate shopping links (a difference that Anthropic's CEO, Dario Amodei, was eager to call out during an interview in Davos last week).

A viral new virtual assistant formerly known as Clawdbot is complex and brings security risks--but some early adopters say it feels like the future. Dan Peguine, a tech entrepreneur and marketing consultant based in Lisbon, lets a precocious, lobster-themed AI assistant called Moltbot run much of his life. Peguine, a self-professed early adopter and trendspotter, discovered Moltbot several weeks ago--back then it was Clawdbot--after discussing a vibe-coding side project with friends on WhatsApp. He installed it on his computer, connected it to numerous apps and online accounts, including Google Apps, and was astonished by how capable it was. "I tried it, got interested, then got really obsessed," Peguine says.

Two important things happened on January 20, 2025. In Washington, D.C., Donald Trump was inaugurated as President of the United States. In Hangzhou, China, a little-known Chinese firm called DeepSeek released R1, an AI model that industry watchers called a "Sputnik moment" for the country's AI industry. "Whether we like it or not, we're suddenly engaged in a fast-paced competition to build and define this groundbreaking technology that will determine so much about the future of civilization," said Trump later that year, as he announced his administration's AI action plan, which was titled "Winning the Race." There are many interpretations of what AI companies and their governments are racing towards, says AI policy researcher Lennart Heim: to deploy AI systems in the economy, to build robots, to create human-like artificial general intelligence.

Google's latest addition to its Chrome browser puts generative AI behind the wheel and you in the passenger seat. Google debuted a new "Auto Browse" feature for Chrome on Wednesday. The tool, powered by Google's current Gemini 3 generative AI model, is an AI agent designed to take over your Chrome browser to help complete online tasks like booking flights, finding apartments, and filing expenses. The release of Auto Browse is part of Google's continued integration of AI features into Chrome. Last year, Google dropped the "Gemini in Chrome" mode to answer questions about what's on web pages and synthesize details from multiple open tabs.

The human genome runs to 3bn pairs of letters - the Gs, Ts, Cs and As that comprise the DNA code. The human genome runs to 3bn pairs of letters - the Gs, Ts, Cs and As that comprise the DNA code. Researchers at Google DeepMind have unveiled their latest artificial intelligence tool and claimed it will help scientists identify the genetic drivers of disease and ultimately pave the way for new treatments. AlphaGenome predicts how mutations interfere with the way genes are controlled, changing when they are switched on, in which cells of the body, and whether their biological volume controls are set to high or low. Most common diseases that run in families, including heart disease and autoimmune disorders, as well as mental health problems, have been linked to mutations that affect gene regulation, as have many cancers, but identifying which genetic glitches are to blame is far from straightforward.

AI model from Google's DeepMind reads recipe for life in DNA An AI model developed by Google's DeepMind could transform our understanding of DNA - the complete recipe for building and running the human body - and its impact on disease and medicine discovery, according to researchers. Called AlphaGenome, the model could help scientists discover why subtle differences in our DNA put us at risk of conditions such as high blood pressure, dementia and obesity. It could also dramatically accelerate our understanding of genetic diseases and cancer. The developers of the model acknowledge it's not perfect, but experts have described it as an incredible feat and a major milestone. We see AlphaGenome as a tool for understanding what the functional elements in the genome do, which we hope will accelerate our fundamental understanding of the code of life, says Natasha Latysheva, research engineer at DeepMind.