Major leap towards reanimation after death as mammal's brain preserved A pig's brain has been frozen with its cellular activity locked in place and minimal damage. Could our brains one day be preserved in a way that locks in our thoughts, feelings and perceptions? An entire mammalian brain has been successfully preserved using a technique that will now be offered to people who are terminally ill. The intention is to preserve all the neural information thought necessary to one day reconstruct the mind of the person it once belonged to. "They would need to donate their brain and body for scientific research," says Borys Wróbel at Nectome in San Francisco, California, a research company focused on memory preservation.

Plus: OpenAI is also creating a super app. OpenAI has a new grand challenge: building an AI researcher--a fully automated agent-based system capable of tackling large, complex problems by itself. The San Francisco firm said the new goal will be its "north star" for the next few years. By September, the company plans to build "an autonomous AI research intern" that can take on a small number of specific research problems. The intern will be the precursor to the fully automated multi-agent system, which is slated to debut in 2028. In an exclusive interview this week, OpenAI's chief scientist, Jakub Pachocki, talked me through the plans.

Placebo and "knowcebo" effects are a problem. But they can also help people feel better. This week I want to look at where we are with psychedelics, the mind-altering substances that have somehow made the leap from counterculture to major focus of clinical research. Compounds like psilocybin--which is found in magic mushrooms--are being explored for all sorts of health applications, including treatments for depression, PTSD, addiction, and even obesity. Over the last decade, we've seen scientific interest in these drugs explode. But most clinical trials of psychedelics have been small and plagued by challenges.

I'm standing in front of a quantum computer built out of atoms and light at the UK's National Quantum Computing Centre on the outskirts of Oxford. On a laboratory table, a complex matrix of mirrors and lenses surrounds a Rubik's Cube-size cell where 100 cesium atoms are suspended in grid formation by a carefully manipulated laser beam. The cesium atom setup is so compact that I could pick it up, carry it out of the lab, and put it on the backseat of my car to take home. I'd be unlikely to get very far, though.

Register now free-of-charge to explore this white paper This Whitepaper offers engineers and researchers a technical examination of the key design barriers in humanoid robotics and the component-level strategies emerging to address them, from sensing and motion control to power systems and thermal management. What you will learn about:   The core engineering challenges — complex motion control, safe human-robot interaction, and hardware cost constraints — that currently limit practical humanoid robot deployment. Sensing system architectures: how IMUs, gyroscopes, accelerometers, tactile sensors, and AMR magnetic sensors support real-time posture estimation, perception fusion, and environmental awareness. Motion and actuation design considerations including actuator-level power delivery, motor noise mitigation, PCB bend-stress resistance, and dexterous hand integration. Power and thermal system trade-offs: battery chemistry selection (LFP vs. NCA), BMS design, DC/DC converter topologies, and thermistor-based protection for operational reliability. Click 'LOOK INSIDE' to Download Now.

Gooey birch tar helped our distant cousins make weapons and possibly treat wounds. The bark of birch trees has been used to produce tar for more than 150,000 years. The center photo shows birch bark tar condensed onto a rock that borders a hearth. When scraped off the rocks, the viscous tar can be used as both an adhesive and antibiotic. Breakthroughs, discoveries, and DIY tips sent six days a week.

We propose an alternative framework to existing setups for controlling false alarms when multiple A/B tests are run over time. This setup arises in many practical applications, e.g. when pharmaceutical companies test new treatment options against control pills for different diseases, or when internet companies test their default webpages versus various alternatives over time. Our framework proposes to replace a sequence of A/B tests by a sequence of best-arm MAB instances, which can be continuously monitored by the data scientist. When interleaving the MAB tests with an online false discovery rate (FDR) algorithm, we can obtain the best of both worlds: low sample complexity and any time online FDR control. Our main contributions are: (i) to propose reasonable definitions of a null hypothesis for MAB instances; (ii) to demonstrate how one can derive an always-valid sequential p-value that allows continuous monitoring of each MAB test; and (iii) to show that using rejection thresholds of online-FDR algorithms as the confidence levels for the MAB algorithms results in both sample-optimality, high power and low FDR at any point in time. We run extensive simulations to verify our claims, and also report results on real data collected from the New Yorker Cartoon Caption contest.

The past decade has seen a revolution in genomic technologies that enabled a flood of genome-wide profiling of chromatin marks. Recent literature tried to understand gene regulation by predicting gene expression from large-scale chromatin measurements. Two fundamental challenges exist for such learning tasks: (1) genome-wide chromatin signals are spatially structured, high-dimensional and highly modular; and (2) the core aim is to understand what are the relevant factors and how they work together. Previous studies either failed to model complex dependencies among input signals or relied on separate feature analysis to explain the decisions. This paper presents an attention-based deep learning approach; AttentiveChrome, that uses a unified architecture to model and to interpret dependencies among chromatin factors for controlling gene regulation. AttentiveChrome uses a hierarchy of multiple Long Short-Term Memory (LSTM) modules to encode the input signals and to model how various chromatin marks cooperate automatically. AttentiveChrome trains two levels of attention jointly with the target prediction, enabling it to attend differentially to relevant marks and to locate important positions per mark. We evaluate the model across 56 different cell types (tasks) in human. Not only is the proposed architecture more accurate, but its attention scores also provide a better interpretation than state-of-the-art feature visualization methods such as saliency map.

Your Out-of-Distribution Detection Method is Not Robust!

For many classic structured prediction problems, probability distributions over the dependent variables can be efficiently computed using widely-known algorithms and data structures (such as forward-backward, and its corresponding trellis for exact probability distributions in Markov models). However, we know of no previous work studying efficient representations of exact distributions over clusterings. This paper presents definitions and proofs for a dynamic-programming inference procedure that computes the partition function, the marginal probability of a cluster, and the MAP clustering---all exactly. Rather than the Nth Bell number, these exact solutions take time and space proportional to the substantially smaller powerset of N. Indeed, we improve upon the time complexity of the algorithm introduced by Kohonen and Corander (2016) for this problem by a factor of N. While still large, this previously unknown result is intellectually interesting in its own right, makes feasible exact inference for important real-world small data applications (such as medicine), and provides a natural stepping stone towards sparse-trellis approximations that enable further scalability (which we also explore). In experiments, we demonstrate the superiority of our approach over approximate methods in analyzing real-world gene expression data used in cancer treatment.