Gene-edited pig kidneys could offer a lifeline to patients stuck waiting for a human donor. A woman has lived with a pig kidney for more than nine months without needing dialysis, a record-setting achievement. The woman underwent the operation at Massachusetts General Hospital on November 22 last year. As of Thursday, the kidney has been functioning for 285 days, according to Cambridge, Massachusetts-based eGenesis, the biotech company that used Crispr gene editing to make the organ fit for human transplantation. Previously, the record for a person living with a transplanted pig kidney was 271 days, set by Tim Andrews. Most people in need of a kidney transplant wait three to five years on average to receive one, though it can take longer depending on blood type and other factors.

During a recent visit to Generalist AI, I watched a robotic arm improvise and use a banana as a tool. Last week, I ventured a whopping 15 minutes from my house to see robots do some mind-boggling, jaw-dropping stuff. I visited the Cambridge, Massachusetts, offices of a startup called Generalist AI, where I watched robot arms perform simple chores like stacking cups, putting blocks into bowls, and the like. I was astonished by how quickly they figured things out--it was reminiscent of a flesh-and-blood person. The arms mastered a range of tasks after ingesting a short, instructional video and, most impressively, no specific training for a given task.

Are you married to a man who's obsessed with AI? If i had to listen to another minute of my husband talking about Claude Code, I might have actually died. It was 11 pm in Berkeley, California, where I was home alone with our 10-month-old daughter, and 2 am in Cambridge, Massachusetts, where he was visiting for his newish job in AI. "JUST LOOK AT THIS!" he shouted. The FaceTime camera zoomed toward a laptop sitting on a hotel bed. I still had to take the dog out. "ARE YOU LOOKING?" he shouted again. I was looking at our real baby. There are two babies in this household now: the small human one and the large language model.

We consider experiments in dynamical systems where interventions on some experimental units impact other units through a limiting constraint (such as a limited supply of products). Despite outsize practical importance, the best estimators for this'Markovian' interference problem are largely heuristic in nature, and their bias is not well understood.

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 present a practical solution with performance guarantees to the problem of dimensionality reduction for very large scale sparse matrices. We show applications of our approach to computing the Principle Component Analysis (PCA) of any n dmatrix, using one pass over the stream of its rows. Our solution uses coresets: a scaled subset of the n rows that approximates their sum of squared distances to every k-dimensional affine subspace. An open theoretical problem has been to compute such a coreset that is independent of both n and d. An open practical problem has been to compute a non-trivial approximation to the PCA of very large but sparse databases such as the Wikipedia document-term matrix in a reasonable time. We answer both of these questions affirmatively. Our main technical result is a new framework for deterministic coreset constructions based on a reduction to the problem of counting items in a stream.