旨在玩图灵的模仿游戏的人工智能，也不是为了最大程度地提高人类对信息操纵而构建的增强情报，以加速创新并改善人类对其最大挑战的集体进步。我们重新概念化并进行了试点AI，可以通过补充人类认知能力来从根本上增强人类的理解。我们的互补情报方法建立在人群智慧的基础上，这取决于人群成员的信息和方法的独立性和多样性。通过将有关科学专业知识不断发展的科学专业知识分布的信息结合在一起，我们的方法遵循文献中内容的分布，同时避免了科学人群和假设可供选择。我们使用这种方法来生成有价值的预测，这些预测具有有价值的能源相关特性（例如，热电学），以及哪些化合物具有补充人类科学人群的有价值的医疗特性（例如，哮喘）。我们证明，如果人类科学家和发明者的确定，我们的互补预测只会在未来进一步发现几年。当我们通过第一原理方程评估预测的承诺时，我们证明了预测的互补性的增加不会减少，在某些情况下会增加预测具有目标特性的概率。总而言之，通过调整AI避免人群，我们可以产生假设，直到遥远的未来，并承诺将科学进步打断。通过确定和纠正集体人类偏见，这些模型还提出了通过重新提高科学教育的发现来改善人类预测的机会。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

语言是沟通人类信息的主要媒体，实现协调。最重要的语言函数之一是将世界分类，因此可以通过对话传达消息。虽然我们对人类语言如何在他们的语义域内的信息中变化时所知，但在颜色，声音，数量，机器人，时间，空间，人类活动，性别，身体部位和生物学中，人类如何变化，但对于全球结构知之甚少语义信息及其对人类交流的影响。使用大规模计算，人工智能技术和跨越15个主题领域的大规模，平行的语料 - 包括宗教，经济学，医学，娱乐，政治和技术 - 在999语言中，我们在这里显示了信息和语义的大量变化语言密度及其对人类沟通与协调的影响。与事先工作相比，我们展示了较高的密度语言相对于较低密度语言更快地传达信息。然后，在14种语言中使用超过9,000个现实生活对话和跨越140种语言的90,000个Wikipedia文章，我们表明，由于有更多方法可以在更密集的语言，对话和文章回溯中讨论任何给定的主题，并通过较窄的概念地形来循环。这些结果表明，对人类交流渠道的一个重要变异来源，表明语言结构塑造了对话的性质和纹理，对群体，组织，市场和社会的行为具有重要影响。

历史流程表现出显着的多样性。尽管如此，学者们长期以来一直试图识别模式，并将历史行动者分类和对一些成功的影响。随机过程框架提供了一种结构化方法，用于分析大型历史数据集，允许检测有时令人惊讶的模式，鉴定内源性和外源对过程的相关因果作用者，以及不同历史案例的比较。随机过程的数据，分析工具和组织理论框架的组合使历史和考古中的传统叙事方法补充了传统的叙事方法。

实质性奖学金估计了工作对自动化的敏感性，但是很少有人研究了作为新技术代替任务，转移所需技能而不是消除整个工作的新技术，就业年龄中的工作内容如何发展。在这里，我们探讨了职业技能内容变化的模式和后果，并表征职业和工人受到最大的重新技能压力。最近的研究表明，高技能的STEM和技术密集型职业经历了技能内容的最高变化率。在2010年至2018年之间，分析了涵盖美国在线劳动力市场近乎宇宙的1.67亿个职业职位的727个职业，我们发现，对于低技能的职业来说，重新技能距离的压力要高得多，无论如何``低技能''是按技能，薪酬水平或教育学位定义的。我们研究了不平衡的职业技能对工人的含义，发现来自大型劳动力市场和大型雇主的人的变化较小，而低技能工作的非白人男性在人口统计学上是最脆弱的。我们通过讨论我们的技能嵌入模型的广泛潜力来结束，该模型从工作职位跨职位的技能共同占领中学习了技能接近，并将其表示为复杂人力资本的高维空间中的距离，这与工人的技能成本相对应。该模型提供了对工作发展的程度的精细度量，并指示工作在哪个方向发展，如人类界面技能的需求下降所示，以及机器接口处的人的上升。

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

Extracting complex structures from grid-based data is a common key step in automated medical image analysis. The conventional solution to recovering tree-structured geometries typically involves computing the minimal cost path through intermediate representations derived from segmentation masks. However, this methodology has significant limitations in the context of projective imaging of tree-structured 3D anatomical data such as coronary arteries, since there are often overlapping branches in the 2D projection. In this work, we propose a novel approach to predicting tree connectivity structure which reformulates the task as an optimization problem over individual steps of a recursive process. We design and train a two-stage model which leverages the UNet and Transformer architectures and introduces an image-based prompting technique. Our proposed method achieves compelling results on a pair of synthetic datasets, and outperforms a shortest-path baseline.

Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.

Grasping is an incredible ability of animals using their arms and limbs in their daily life. The human hand is an especially astonishing multi-fingered tool for precise grasping, which helped humans to develop the modern world. The implementation of the human grasp to virtual reality and telerobotics is always interesting and challenging at the same time. In this work, authors surveyed, studied, and analyzed the human hand-grasping behavior for the possibilities of haptic grasping in the virtual and remote environment. This work is focused on the motion and force analysis of fingers in human hand grasping scenarios and the paper describes the transition of the human hand grasping towards a tripod haptic grasp model for effective interaction in virtual reality.