Recent memoirs by the retired bookie Art Manteris and the storied gambler Billy Walters provide a glimpse of an industry in its fledgling form--and a preview of the DraftKings era to come. Las Vegas is no longer the seat of the sportsbook gods. In most states, it's now legal, and extremely popular, to place bets using apps or websites such as FanDuel and DraftKings. From your couch, you can wager on everything from the results of snooker championships to the color of the Gatorade poured over the victorious coach after the Super Bowl. The N.F.L., along with the other major-league American sports associations, has officially partnered with sports-betting sites, and their alliance has proved so lucrative that other industries want in on the action; last month, the Golden Globes made a deal with Polymarket, a predictions-market platform, to encourage wagering (or "trading," if you prefer) on the outcomes of its awards race.

'It brings you closer to the natural world': the rise of the Merlin birdsong identifying app W hen Natasha Walter first became curious about the birds around her, she recorded their songs on her phone and arduously tried to match each song with online recordings. After a friend recommended Merlin Bird ID, a free app, she tried it in her London garden and was delighted to discover the birds she assumed were female blackbirds - "this is how bad a birder I was" - were actually song thrushes and mistle thrushes. "I'm obsessed with Merlin - it's wonderful and it's been a joy to me," says Walter, a writer and human rights activist. "This is what AI and machine-learning have been invented for. Merlin is having a moment. The app, developed by the Cornell Lab of Ornithology in New York, which listens for birdsong and identifies the species singing, has been downloaded 33m times, in 240 countries and territories around the world. Britain has the second highest total number of users - more than 1.5 million in 2024, an 88% increase from 2023. Every month, there has been a 30% increase in new users of the app, whose sound identification function was launched in 2021. Merlin has been trained to identify the songs of more than 1,300 species around the world, with more birds added twice a year. Different songs make distinct patterns on spectrograms and Merlin is trained to recognise these different shapes and attribute them to a species. For latecomers to birding, or those lacking a knowledgeable friend, the app has become their teacher. "My fear at first was I wouldn't actually learn because I'm outsourcing my understanding of birds to this app," says Walter. "But that hasn't come to pass.

June Squibb sparkles opposite Cynthia Nixon in a futuristic drama, and Michelle Williams loses her way in Eugene O'Neill's Pulitzer Prize winner. Appropriately enough, Jordan Harrison's déjà-vu-inducing "Marjorie Prime" has been here before. The Off Broadway theatre Playwrights Horizons produced the poignant sci-fi play about hyperrealistic re-creations of the dead--so-called Primes, which are used as a supportive technology for the bereaved--in Anne Kauffman's spirited, delicately comic production, back in 2015. Lois Smith, then eighty-five years old, played Marjorie, a woman struggling with dementia. It's the early twenty-sixties, and so Marjorie is attended by a holographic Prime of her husband, Walter, who tells her stories from her own life.

However, the high cost of tactile data collection makes sample efficiency the key requirement for developing visuotactile policies. We present EquiT ac, a framework that exploits the inherent SO(2) symmetry of in-hand object rotation to improve sample efficiency and generalization for visuotactile policy learning. EquiT ac first reconstructs surface normals from raw RGB inputs of vision-based tactile sensors, so rotations of the normal vector field correspond to in-hand object rotations. An SO(2)- equivariant network then predicts a residual rotation action that augments a base visuomotor policy at test time, enabling real-time rotation correction without additional reorientation demonstrations. On a real robot, EquiT ac accurately achieves robust zero-shot generalization to unseen in-hand orientations with very few training samples, where baselines fail even with more training data. T o our knowledge, this is the first tactile learning method to explicitly encode tactile equivari-ance for policy learning, yielding a lightweight, symmetry-aware module that improves reliability in contact-rich tasks.

We present Generalizable Hierarchical Skill Learning (GSL), a novel framework for hierarchical policy learning that significantly improves policy generalization and sample efficiency in robot manipulation. One core idea of GSL is to use object-centric skills as an interface that bridges the high-level vision-language model and the low-level visual-motor policy. Specifically, GSL decomposes demonstrations into transferable and object-canonicalized skill primitives using foundation models, ensuring efficient low-level skill learning in the object frame. At test time, the skill-object pairs predicted by the high-level agent are fed to the low-level module, where the inferred canonical actions are mapped back to the world frame for execution. This structured yet flexible design leads to substantial improvements in sample efficiency and generalization of our method across unseen spatial arrangements, object appearances, and task compositions. In simulation, GSL trained with only 3 demonstrations per task outperforms baselines trained with 30 times more data by 15.5 percent on unseen tasks. In real-world experiments, GSL also surpasses the baseline trained with 10 times more data.

A novel approach for the fast onboard detection of isolated markers for visual relative localisation of multiple teammates in agile UAV swarms is introduced in this paper. As the detection forms a key component of real-time localisation systems, a three-fold innovation is presented, consisting of an optimised procedure for CPUs, a GPU shader program, and a functionally equivalent FPGA streaming architecture. For the proposed CPU and GPU solutions, the mean processing time per pixel of input camera frames was accelerated by two to three orders of magnitude compared to the \rev{unoptimised state-of-the-art approach}. For the localisation task, the proposed FPGA architecture offered the most significant overall acceleration by minimising the total delay from camera exposure to detection results. Additionally, the proposed solutions were evaluated on various 32-bit and 64-bit embedded platforms to demonstrate their efficiency, as well as their feasibility for applications using low-end UAVs and MAVs. Thus, it has become a crucial enabling technology for agile UAV swarming.

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Diffusion Policies are effective at learning closed-loop manipulation policies from human demonstrations but generalize poorly to novel arrangements of objects in 3D space, hurting real-world performance. To address this issue, we propose Spherical Diffusion Policy (SDP), an SE(3) equivariant diffusion policy that adapts trajectories according to 3D transformations of the scene. Such equivariance is achieved by embedding the states, actions, and the denoising process in spherical Fourier space. Additionally, we employ novel spherical FiLM layers to condition the action denoising process equivariantly on the scene embeddings. Lastly, we propose a spherical denoising temporal U-net that achieves spatiotemporal equivariance with computational efficiency. In the end, SDP is end-to-end SE(3) equivariant, allowing robust generalization across transformed 3D scenes. SDP demonstrates a large performance improvement over strong baselines in 20 simulation tasks and 5 physical robot tasks including single-arm and bi-manual embodiments. Code is available at https://github.com/amazon-science/Spherical_Diffusion_Policy.

Yurts founder and CEO Ben Van Roo breaks down concerns over DeepSeek on'The Will Cain Show.' Artificial intelligence may be the new frontier for childhood schooling, but the idea of teacherless classrooms has received mixed reviews from state education officials. Unbound Academy, a Texas-based institution billing itself as the nation's first virtual, tuition-free charter school for grades 4 through 8, reportedly employs AI to teach students in a way that can be geared toward the individual student without "frustration[s]" sometimes present in traditional schooling. While such schools have seen success in being approved to educate students in Arizona, Unbound was formally rejected by the Pennsylvania Department of Education in a letter obtained by Fox News Digital. In a letter to an Unbound Academy official with a Lancaster office address, Secretary Angela Fitterer said her office has found "deficiencies" in all five criteria needed for approval to teach Keystone State students. Pennsylvania's Charter School law denotes a school must demonstrate sustainable support for the cyber charter school plan from teachers, parents and students.

Recent advances in Keyframe Imitation Learning (IL) have enabled learning-based agents to solve a diverse range of manipulation tasks. However, most approaches ignore the rich symmetries in the problem setting and, as a consequence, are sample-inefficient. This work identifies and utilizes the bi-equivariant symmetry within Keyframe IL to design a policy that generalizes to transformations of both the workspace and the objects grasped by the gripper. We make two main contributions: First, we analyze the bi-equivariance properties of the keyframe action scheme and propose a Keyframe Transporter derived from the Transporter Networks, which evaluates actions using cross-correlation between the features of the grasped object and the features of the scene. Second, we propose a computationally efficient coarse-to-fine SE(3) action evaluation scheme for reasoning the intertwined translation and rotation action. The resulting method outperforms strong Keyframe IL baselines by an average of >10% on a wide range of simulation tasks, and by an average of 55% in 4 physical experiments.