Point-of-Care Ultrasound (POCUS) refers to clinician-performed and interpreted ultrasonography at the patient's bedside. Interpreting these images requires a high level of expertise, which may not be available during emergencies. In this paper, we support POCUS by developing classifiers that can aid medical professionals by diagnosing whether or not a patient has pneumothorax. We decomposed the task into multiple steps, using YOLOv4 to extract relevant regions of the video and a 3D sparse coding model to represent video features. Given the difficulty in acquiring positive training videos, we trained a small-data classifier with a maximum of 15 positive and 32 negative examples. To counteract this limitation, we leveraged subject matter expert (SME) knowledge to limit the hypothesis space, thus reducing the cost of data collection. We present results using two lung ultrasound datasets and demonstrate that our model is capable of achieving performance on par with SMEs in pneumothorax identification. We then developed an iOS application that runs our full system in less than 4 seconds on an iPad Pro, and less than 8 seconds on an iPhone 13 Pro, labeling key regions in the lung sonogram to provide interpretable diagnoses.

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.

许多最近的自我监督学习方法在图像分类和其他任务上表现出了令人印象深刻的表现。已经使用了一种令人困惑的多种技术，并不总是清楚地了解其收益的原因，尤其是在组合使用时。在这里，我们将图像的嵌入视为点粒子，并将模型优化视为该粒子系统上的动态过程。我们的动态模型结合了类似图像的吸引力，避免局部崩溃的局部分散力以及实现颗粒的全球均匀分布的全局分散力。动态透视图突出了使用延迟参数图像嵌入（a la byol）以及同一图像的多个视图的优点。它还使用纯动态的局部分散力（布朗运动），该分散力比其他方法显示出改善的性能，并且不需要其他粒子坐标的知识。该方法称为MSBREG，代表（i）多视质心损失，它施加了吸引力的力来将不同的图像视图嵌入到其质心上，（ii）奇异值损失，将粒子系统推向空间均匀的密度（ iii）布朗扩散损失。我们评估MSBREG在ImageNet上的下游分类性能以及转移学习任务，包括细粒度分类，多类对象分类，对象检测和实例分段。此外，我们还表明，将我们的正则化术语应用于其他方法，进一步改善了其性能并通过防止模式崩溃来稳定训练。

变形金刚已成为主要的机器学习工作负载，它们不仅是自然语言处理任务的事实上的标准，而且还将部署在其他领域，例如视觉和语音识别。许多基于变压器的应用程序都是实时系统，例如机器翻译和Web搜索。这些实时系统通常具有严格的端到端推理潜伏期需求。不幸的是，尽管大多数变压器计算都来自基质乘法，但变压器还包括几种非线性组件，它们在推理过程中倾向于成为瓶颈。在这项工作中，我们加快了张量流处理器上BERT模型的推断。通过小心地将所有非线性组件与矩阵乘法组件融合在一起，我们能够有效地利用芯片矩阵乘法单元，从而通过BERT-1通过BERT-1通过BERT-BASE，确定性的尾巴延迟为130 $ \ MU $ s，比当前的最新时间快6倍。

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。

对象接地任务旨在通过口头通信定位图像中的目标对象。了解人类命令是有效人体机器人通信所需的重要过程。然而，这是具有挑战性的，因为人类命令可能是暧昧和错误的。本文旨在消除人类的引用表达式，允许代理基于从场景图获得的语义数据提出相关问题。我们测试如果我们的代理可以从场景图之间使用对象之间的关系，以便询问可以消除原始用户命令的语义相关问题。在本文中，我们使用场景图（IGSG）提出增量接地，消歧模型使用从图像场景图和语言场景图到基于人类命令的地面对象的语义数据的歧义模型。与基线相比，IGSG显示了有希望的成果，在有多个相同的目标对象的复杂现实场景中。 IGSG可以通过要求消除歧义问题回到用户来有效消除歧义或错误的表达式。

我们微调GPT-3使用基于文本的Web浏览环境来回答长形问题，允许模型搜索和导航Web。通过建立任务，以便通过人类执行，我们能够使用模仿学习培训在任务上的模型，然后通过人体反馈优化答案质量。为了使人为评估事实精度更容易，模型必须在浏览支持答案时收集引用。我们在ELI5上培训并评估我们的模型，Reddit用户提出的问题数据集。我们的最佳模型是通过使用行为克隆进行微调GPT-3获得的，然后对训练训练的奖励模型进行拒绝采样来获得以预测人类偏好。这种模式的答案是人类56％的答案，我们的人类示威者的时间和69％的时间到Reddit的最高投票答复。

We demonstrate a proof-of-concept of a large language model conducting corporate lobbying related activities. We use an autoregressive large language model (OpenAI's text-davinci-003) to determine if proposed U.S. Congressional bills are relevant to specific public companies and provide explanations and confidence levels. For the bills the model deems as relevant, the model drafts a letter to the sponsor of the bill in an attempt to persuade the congressperson to make changes to the proposed legislation. We use hundreds of ground-truth labels of the relevance of a bill to a company to benchmark the performance of the model, which outperforms the baseline of predicting the most common outcome of irrelevance. However, we test the ability to determine the relevance of a bill with the previous OpenAI GPT-3 model (text-davinci-002), which was state-of-the-art on many language tasks until text-davinci-003 was released on November 28, 2022. The performance of text-davinci-002 is worse than simply always predicting that a bill is irrelevant to a company. These results suggest that, as large language models continue to improve core natural language understanding capabilities, performance on corporate lobbying related tasks will continue to improve. We then discuss why this could be problematic for societal-AI alignment.

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.

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.