Geographic context is often consider relevant to motor insurance risk, yet public actuarial datasets provide limited location identifiers, constraining how this information can be incorporated and evaluated in claim-frequency models. This study examines how geographic information from alternative data sources can be incorporated into actuarial models for Motor Third Party Liability (MTPL) claim prediction under such constraints. Using the BeMTPL97 dataset, we adopt a zone-level modeling framework and evaluate predictive performance on unseen postcodes. Geographic information is introduced through two channels: environmental indicators from OpenStreetMap and CORINE Land Cover, and orthoimagery released by the Belgian National Geographic Institute for academic use. We evaluate the predictive contribution of coordinates, environmental features, and image embeddings across three baseline models: generalized linear models (GLMs), regularized GLMs, and gradient-boosted trees, while raw imagery is modeled using convolutional neural networks. Our results show that augmenting actuarial variables with constructed geographic information improves accuracy. Across experiments, both linear and tree-based models benefit most from combining coordinates with environmental features extracted at 5 km scale, while smaller neighborhoods also improve baseline specifications. Generally, image embeddings do not improve performance when environmental features are available; however, when such features are absent, pretrained vision-transformer embeddings enhance accuracy and stability for regularized GLMs. Our results show that the predictive value of geographic information in zone-level MTPL frequency models depends less on model complexity than on how geography is represented, and illustrate that geographic context can be incorporated despite limited individual-level spatial information.

In this paper, we make the case that a scientific theory of deep learning is emerging. By this we mean a theory which characterizes important properties and statistics of the training process, hidden representations, final weights, and performance of neural networks. We pull together major strands of ongoing research in deep learning theory and identify five growing bodies of work that point toward such a theory: (a) solvable idealized settings that provide intuition for learning dynamics in realistic systems; (b) tractable limits that reveal insights into fundamental learning phenomena; (c) simple mathematical laws that capture important macroscopic observables; (d) theories of hyperparameters that disentangle them from the rest of the training process, leaving simpler systems behind; and (e) universal behaviors shared across systems and settings which clarify which phenomena call for explanation. Taken together, these bodies of work share certain broad traits: they are concerned with the dynamics of the training process; they primarily seek to describe coarse aggregate statistics; and they emphasize falsifiable quantitative predictions. We argue that the emerging theory is best thought of as a mechanics of the learning process, and suggest the name learning mechanics. We discuss the relationship between this mechanics perspective and other approaches for building a theory of deep learning, including the statistical and information-theoretic perspectives. In particular, we anticipate a symbiotic relationship between learning mechanics and mechanistic interpretability. We also review and address common arguments that fundamental theory will not be possible or is not important. We conclude with a portrait of important open directions in learning mechanics and advice for beginners. We host further introductory materials, perspectives, and open questions at learningmechanics.pub.

Army infantry drone operators successfully test the bunker rupture and kinetic explosive round, delivered by an unmanned aerial system, during a live-fire demonstration at Redstone Arsenal, Ala., March 26, 2026. Get your news from a source that's not owned and controlled by oligarchs. The US Army announced this week that it has successfully 3D-printed a drone-based warhead prototype, and successfully used that weapon to make something explode. In a press release, the military called the weapon "a lightweight, powerful, and lethal warhead that could be deployed from a small, agile drone." In a video posted April 21 and captioned only "Multi-Purpose," a drone blows up a makeshift bunker on a military testing site.

Drone incursions over U.S. military bases reveal critical vulnerabilities in civilian infrastructure, from airports to energy grids, that experts say remain dangerously exposed.

A nature-loving 10-year-old girl who found an endangered amphibian under a bridge has left her mum in shock, surprise and disbelief. Melanie Hill said her daughter, Evie, discovered the nine-inch Mexican axolotl as they spent the day near the River Ogmore in Bridgend. She said Evie was always finding things like newts and bugs, but said the axolotl discovery was a surprise. It is the first documented discovery of an axolotl in the wild in the UK with only 50 to 1,000 individuals left globally today, according to experts. Axolotls as pets have seen a surge in popularity in recent years after they were introduced to video games such as Minecraft and Roblox.

Plus: An unauthorized group has reportedly accessed Anthropic's Mythos. What actually matters in AI right now? It's getting harder to tell amid the constant launches, hype, and warnings. To cut through the noise, reporters and editors have distilled years of analysis into a new essential guide: the 10 Things That Matter in AI Right Now . The list builds on our annual 10 Breakthrough Technologies, but takes a wider view of the ideas, topics, and research shaping AI, spotlighting the trends and breakthroughs shaping the world. We'll be unpacking one item from the list each day here in The Download, explaining what it means and why it matters.

McDonald's boss on abuse claims: 'I don't want to talk about the past' The boss of McDonald's UK and Ireland has said she doesn't want to talk about the past when asked about allegations of abuse at the fast-food chain. Lauren Schultz told the BBC what had happened in recent years was unacceptable but said we have drawn a line under it. A BBC investigation in 2023 heard from more than 100 McDonald's workers in the UK claiming they faced a toxic culture of sexual assault, harassment, racism, and bullying. Last year, staff said they still faced sexual abuse and harassment. The UK equality watchdog agreed tougher measures with the company to protect staff in November, including new sexual harassment training.

Anthropic is investigating a claim that a small group of people gained access to its Claude Mythos model - the cyber-security tool which the AI firm says is too powerful to release to the public. We're investigating a report claiming unauthorized access to Claude Mythos Preview through one of our third-party vendor environments, the company said in a statement. It was in response to a Bloomberg report that users in a private forum managed to access the model without the normal permissions. There is deep unease about Mythos' capabilities - though the UK's top cyber official has said advanced AI tools could be a net positive if the technology was secured from misuse. There is currently no suggestion that malicious actors have managed to get hold of the model, and Anthropic says it does not have evidence its systems are affected.

One town's scheme to get rid of its geese Public officials in one California burgh spent nearly $400,000 on tech to flush out waterfowl. Some geese, like the one on the left, wear GPS trackers as part of the Foster City goose management plan. Our target is in sight: a gaggle of Canada geese, pecking at grass near the dog park. As I approach, tiptoeing over their grayish-white poop, I notice that one bird wears a white cuff around its slender black neck. It's a GPS tracker--part of a new tech-centered campaign to drive the geese out of my hometown of Foster City, California. About 300 geese live in this sleepy Bay Area suburb, equal to nearly 1% of our human population--and some say this town isn't big enough for the both of us.

The Pope's Warnings About AI Were AI-Generated, a Detection Tool Claims Pangram Labs' updated Chrome extension puts warning labels on AI slop as you scroll your social feeds. On Monday, a brand-new Reddit account popped up on the widely read forum r/AmItheAsshole, where users have their personal disputes arbitrated by strangers. This particular user asked if they had crossed a line by "refusing to babysit my stepmother's kids because I have my own job and responsibilities." The post itself was succinct, straightforward, and grammatically clean, explaining a situation in which the person's stepmother and father often expected them to provide childcare on little notice, eventually leading to an argument. "Now there's tension at home, and I'm starting to wonder if I handled it the wrong way," the redditor concluded.