通过将云资源转换为用户的邻近来减轻云计算所拥有的限制来引入雾计算。雾环境使其有限的资源可用于大量用户部署其无服务器的应用程序，由多个无服务器功能组成。引入迷雾环境背后的主要意图是通过其有限的资源来满足延迟和位置敏感无服务器应用程序的需求。最近的研究主要侧重于将最大资源分配给来自FOG节点的这些应用程序，而不是充分利用云环境。这引入了在将资源提供给最大连接用户的负面影响。为了解决此问题，在本文中，我们调查了用户请求的最佳百分比，该请求应由雾和云实现。因此，我们提出了Def-Driel，系统地部署了使用深度增强学习的雾和云环境中无服务器功能，使用若干现实生活参数，例如来自附近FOG节点，用户的优先级的用户的距离和延迟，与最近的相关算法相比，无服务器应用程序的优先级及其资源需求等。从模拟和比较结果，可以清楚地观察到其对其他算法的优势及其对现实生活场景的适用性。

Step-by-step reasoning approaches like chain-of-thought (CoT) have proved to be a very effective technique to induce reasoning capabilities in large language models. However, the success of the CoT approach depends primarily on model size, and often billion parameter-scale models are needed to get CoT to work. In this paper, we propose a knowledge distillation approach, that leverages the step-by-step CoT reasoning capabilities of larger models and distils these reasoning abilities into smaller models. Our approach Decompositional Distillation learns a semantic decomposition of the original problem into a sequence of subproblems and uses it to train two models: a) a problem decomposer that learns to decompose the complex reasoning problem into a sequence of simpler sub-problems and b) a problem solver that uses the intermediate subproblems to solve the overall problem. On a multi-step math word problem dataset (GSM8K), we boost the performance of GPT-2 variants up to 35% when distilled with our approach compared to CoT. We show that using our approach, it is possible to train a GPT-2-large model (775M) that can outperform a 10X larger GPT-3 (6B) model trained using CoT reasoning. Finally, we also demonstrate that our approach of problem decomposition can also be used as an alternative to CoT prompting, which boosts the GPT-3 performance by 40% compared to CoT prompts.

We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language. We discuss the dominant datasets within each theme, evaluation metrics for the challenges, and the performance of state-of-the-art models. We highlight commonalities between top approaches to the challenges and identify potential future directions for Embodied AI research.

原则上，将变异自动编码器（VAE）应用于顺序数据提供了一种用于控制序列生成，操纵和结构化表示学习的方法。但是，训练序列VAE具有挑战性：自回归解码器通常可以解释数据而无需使用潜在空间，即后置倒塌。为了减轻这种情况，最新的模型通过将均匀的随机辍学量应用于解码器输入来削弱强大的解码器。从理论上讲，我们表明，这可以消除解码器输入提供的点式互信息，该信息通过利用潜在空间来补偿。然后，我们提出了一种对抗性训练策略，以实现基于信息的随机辍学。与标准文本基准数据集上的均匀辍学相比，我们的目标方法同时提高了序列建模性能和潜在空间中捕获的信息。

变形金刚用大型数据集的扩展能力彻底改变了视力和自然语言处理。但是在机器人的操作中，数据既有限又昂贵。我们仍然可以从具有正确的问题制定的变压器中受益吗？我们用Peract进行了调查，这是一种用于多任务6 DOF操纵的语言条件的行为结合剂。 Peract用感知器变压器编码语言目标和RGB-D Voxel观测值，并通过“检测下一个最佳素素动作”来输出离散的动作。与在2D图像上运行的框架不同，体素化的观察和动作空间为有效学习的6-DOF策略提供了强大的结构性先验。通过此公式，我们训练一个单个多任务变压器，用于18个RLBench任务（具有249个变体）和7个现实世界任务（具有18个变体），从每个任务仅几个演示。我们的结果表明，针对各种桌面任务，佩内的磨损明显优于非结构化图像到作用剂和3D Convnet基准。

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。

We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. ALFRED includes long, compositional tasks with nonreversible state changes to shrink the gap between research benchmarks and real-world applications. ALFRED consists of expert demonstrations in interactive visual environments for 25k natural language directives. These directives contain both high-level goals like "Rinse off a mug and place it in the coffee maker." and low-level language instructions like "Walk to the coffee maker on the right." ALFRED tasks are more complex in terms of sequence length, action space, and language than existing visionand-language task datasets. We show that a baseline model based on recent embodied vision-and-language tasks performs poorly on ALFRED, suggesting that there is significant room for developing innovative grounded visual language understanding models with this benchmark.