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.

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Boosting methods often achieve excellent classification accuracy, but can experience notable performance degradation in the presence of label noise. Existing robust methods for boosting provide theoretical robustness guarantees for certain types of label noise, and can exhibit only moderate performance degradation. However, previous theoretical results do not account for realistic types of noise and finite training sizes, and existing robust methods can provide unsatisfactory accuracies, even without noise. This paper presents methods for robust minimax boosting (RMBoost) that minimize worst-case error probabilities and are robust to general types of label noise. In addition, we provide finite-sample performance guarantees for RMBoost with respect to the error obtained without noise and with respect to the best possible error (Bayes risk). The experimental results corroborate that RMBoost is not only resilient to label noise but can also provide strong classification accuracy.

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.

Within a single sniff, the mammalian olfactory system can decode the identity and concentration of odorants wafted on turbulent plumes of air. Yet, it must do so given access only to the noisy, dimensionally-reduced representation of the odor world provided by olfactory receptor neurons. As a result, the olfactory system must solve a compressed sensing problem, relying on the fact that only a handful of the millions of possible odorants are present in a given scene. Inspired by this principle, past works have proposed normative compressed sensing models for olfactory decoding. However, these models have not captured the unique anatomy and physiology of the olfactory bulb, nor have they shown that sensing can be achieved within the 100-millisecond timescale of a single sniff. Here, we propose a rate-based Poisson compressed sensing circuit model for the olfactory bulb.

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.