Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

本文介绍了更轻松的项目及其范围的概念概述。更轻松地专注于通过半自治的移动操纵器在灾难反应方案中支持紧急部队。具体来说，我们检查了操作员对系统的信任以及其使用所产生的认知负荷。我们计划讨论不同的研究主题，探讨共享的自主权，互动设计和透明度与信任和认知负担如何相关。另一个目标是开发非侵入性方法，以使用多级方法在灾难响应的背景下连续衡量信任和认知负荷。该项目由多个学术合作伙伴进行，专门从事人工智能，互动设计和心理学，以及灾难响应设备的工业合作伙伴和最终用户，用于制定项目和实验实验。

在过去的十年中，强化学习成功地解决了复杂的控制任务和决策问题，例如Go棋盘游戏。然而，在将这些算法部署到现实世界情景方面的成功案例很少。原因之一是在处理和避免不安全状态时缺乏保证，这是关键控制工程系统的基本要求。在本文中，我们介绍了指导性的安全射击（GUS），这是一种基于模型的RL方法，可以学会以最小的侵犯安全限制来控制系统。该模型以迭代批次方式在系统操作过程中收集的数据中学习，然后用于计划在每个时间步骤执行的最佳动作。我们提出了三个不同的安全计划者，一个基于简单的随机拍摄策略，两个基于MAP-ELITE，一种更高级的发散搜索算法。实验表明，这些计划者可以帮助学习代理避免在最大程度地探索状态空间的同时避免不安全的情况，这是学习系统准确模型的必要方面。此外，与无模型方法相比，学习模型可以减少与现实系统的交互作用的数量，同时仍达到高奖励，这是处理工程系统时的基本要求。

Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video. However they lack inductive biases and scale quadratically with input length. These limitations are further exacerbated when dealing with the high dimensionality introduced with the temporal dimension. While there are surveys analyzing the advances of Transformers for vision, none focus on an in-depth analysis of video-specific designs. In this survey we analyze main contributions and trends of works leveraging Transformers to model video. Specifically, we delve into how videos are handled as input-level first. Then, we study the architectural changes made to deal with video more efficiently, reduce redundancy, re-introduce useful inductive biases, and capture long-term temporal dynamics. In addition we provide an overview of different training regimes and explore effective self-supervised learning strategies for video. Finally, we conduct a performance comparison on the most common benchmark for Video Transformers (i.e., action classification), finding them to outperform 3D ConvNets even with less computational complexity.

合成孔径声纳（SAS）图像分辨率受波形带宽和阵列几何结构的约束。具体地，波形带宽确定点扩展函数（PSF），其将点散射体的位置模糊在场景中。理论上，用场景PSF解码重建的SAS图像恢复散射体的原始分布，并产生更清晰的重建。然而，去卷积是一种对噪声高度敏感的不良操作。在这项工作中，我们利用隐式的神经表示（inrs），被证明是自然图像空间的强前沿，以解构SAS图像。重要的是，我们的方法不需要培训数据，因为我们通过以自我监督的方式进行分析逐个合金优化来执行Deconvolution。我们验证了我们在使用点散射模型创建的模拟SAS数据的方法和用空内圆形SA捕获的真实数据。这项工作是应用SAS图像解卷积的神经网络的重要第一步。

最近已被证明大型语言模型在各种任务集中获得合理的零射普通化（Brown等，2020）。它已经假设这是语言模型的隐式多任务学习的结果，在语言模型中的预押（Radford等，2019）。可以通过明确的多任务学习直接引起零拍常规化？为了以缩放测试这个问题，我们开发一个系统，以便轻松地将任何自然语言任务映射到人类可读的提示表单中。我们转换一组大量的监督数据集，每个数据集都有多个提示，具有不同的措辞。这些提示的数据集允许基准测试模型执行完全看不见的任务的能力。我们介绍了一个普拉克尔编码器 - 解码器模型（Raffel等，2020; Lester等，2021），覆盖各种任务。该模型在多个标准数据集中达到强大的零点性能，通常优于其尺寸的型号超过16倍。此外，我们的方法对来自Big-替补基准测试的任务子集具有强烈性能，优于其尺寸的6倍。所有提示和培训的型号都可以在https://github.com/ bigscience-workshop / protectsource / httpsource / https：//huggingface.co/bigscience/t0pp。

现代深度学习应用程序需要越来越多地计算培训最先进的模型。为了解决这一需求，大型企业和机构使用专用的高性能计算集群，其建筑和维护既昂贵又远远超出大多数组织的预算。结果，一些研究方向成为几个大型工业甚至更少的学术作用者的独家领域。为了减轻这种差异，较小的团体可以汇集他们的计算资源并运行有利于所有参与者的协作实验。这种范式称为网格或志愿者计算，在众多科学领域看到了成功的应用。然而，由于高延迟，不对称带宽以及志愿者计算独特的几个挑战，使用这种用于机器学习的方法是困难的。在这项工作中，我们仔细分析了这些约束，并提出了一种专门用于协作培训的新型算法框架。我们展示了我们在现实条件下的SWAV和Albert预先预价的方法的有效性，并在成本的一小部分中实现了与传统设置相当的性能。最后，我们提供了一份成功的协作语言模型预先追溯的详细报告，有40名参与者。

View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they lead to highly non-linear reflection flows as the camera moves. We introduce a new point-based representation to compute Neural Point Catacaustics allowing novel-view synthesis of scenes with curved reflectors, from a set of casually-captured input photos. At the core of our method is a neural warp field that models catacaustic trajectories of reflections, so complex specular effects can be rendered using efficient point splatting in conjunction with a neural renderer. One of our key contributions is the explicit representation of reflections with a reflection point cloud which is displaced by the neural warp field, and a primary point cloud which is optimized to represent the rest of the scene. After a short manual annotation step, our approach allows interactive high-quality renderings of novel views with accurate reflection flow. Additionally, the explicit representation of reflection flow supports several forms of scene manipulation in captured scenes, such as reflection editing, cloning of specular objects, reflection tracking across views, and comfortable stereo viewing. We provide the source code and other supplemental material on https://repo-sam.inria.fr/ fungraph/neural_catacaustics/

Edge computing is changing the face of many industries and services. Common edge computing models offload computing which is prone to security risks and privacy violation. However, advances in deep learning enabled Internet of Things (IoTs) to take decisions and run cognitive tasks locally. This research introduces a decentralized-control edge model where most computation and decisions are moved to the IoT level. The model aims at decreasing communication to the edge which in return enhances efficiency and decreases latency. The model also avoids data transfer which raises security and privacy risks. To examine the model, we developed SAFEMYRIDES, a scene-aware ridesharing monitoring system where smart phones are detecting violations at the runtime. Current real-time monitoring systems are costly and require continuous network connectivity. The system uses optimized deep learning that run locally on IoTs to detect violations in ridesharing and record violation incidences. The system would enhance safety and security in ridesharing without violating privacy.

Cognitive Computing (COC) aims to build highly cognitive machines with low computational resources that respond in real-time. However, scholarly literature shows varying research areas and various interpretations of COC. This calls for a cohesive architecture that delineates the nature of COC. We argue that if Herbert Simon considered the design science is the science of artificial, cognitive systems are the products of cognitive science or 'the newest science of the artificial'. Therefore, building a conceptual basis for COC is an essential step into prospective cognitive computing-based systems. This paper proposes an architecture of COC through analyzing the literature on COC using a myriad of statistical analysis methods. Then, we compare the statistical analysis results with previous qualitative analysis results to confirm our findings. The study also comprehensively surveys the recent research on COC to identify the state of the art and connect the advances in varied research disciplines in COC. The study found that there are three underlaying computing paradigms, Von-Neuman, Neuromorphic Engineering and Quantum Computing, that comprehensively complement the structure of cognitive computation. The research discuss possible applications and open research directions under the COC umbrella.