Generalist models, which are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model, have been explored recently. Being, hopefully, an alternative to approaching general-purpose AI, existing generalist models are still at an early stage, where modality and task coverage is limited. To empower multi-modal task-scaling and speed up this line of research, we release a generalist model learning system, OFASys, built on top of a declarative task interface named multi-modal instruction. At the core of OFASys is the idea of decoupling multi-modal task representations from the underlying model implementations. In OFASys, a task involving multiple modalities can be defined declaratively even with just a single line of code. The system automatically generates task plans from such instructions for training and inference. It also facilitates multi-task training for diverse multi-modal workloads. As a starting point, we provide presets of 7 different modalities and 23 highly-diverse example tasks in OFASys, with which we also develop a first-in-kind, single model, OFA+, that can handle text, image, speech, video, and motion data. The single OFA+ model achieves 95% performance in average with only 16% parameters of 15 task-finetuned models, showcasing the performance reliability of multi-modal task-scaling provided by OFASys. Available at https://github.com/OFA-Sys/OFASys

High-definition (HD) semantic map generation of the environment is an essential component of autonomous driving. Existing methods have achieved good performance in this task by fusing different sensor modalities, such as LiDAR and camera. However, current works are based on raw data or network feature-level fusion and only consider short-range HD map generation, limiting their deployment to realistic autonomous driving applications. In this paper, we focus on the task of building the HD maps in both short ranges, i.e., within 30 m, and also predicting long-range HD maps up to 90 m, which is required by downstream path planning and control tasks to improve the smoothness and safety of autonomous driving. To this end, we propose a novel network named SuperFusion, exploiting the fusion of LiDAR and camera data at multiple levels. We benchmark our SuperFusion on the nuScenes dataset and a self-recorded dataset and show that it outperforms the state-of-the-art baseline methods with large margins. Furthermore, we propose a new metric to evaluate the long-range HD map prediction and apply the generated HD map to a downstream path planning task. The results show that by using the long-range HD maps predicted by our method, we can make better path planning for autonomous vehicles. The code will be available at https://github.com/haomo-ai/SuperFusion.

Regression trees are one of the oldest forms of AI models, and their predictions can be made without a calculator, which makes them broadly useful, particularly for high-stakes applications. Within the large literature on regression trees, there has been little effort towards full provable optimization, mainly due to the computational hardness of the problem. This work proposes a dynamic-programming-with-bounds approach to the construction of provably-optimal sparse regression trees. We leverage a novel lower bound based on an optimal solution to the k-Means clustering algorithm in 1-dimension over the set of labels. We are often able to find optimal sparse trees in seconds, even for challenging datasets that involve large numbers of samples and highly-correlated features.

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.

准确的移动对象细分是自动驾驶的重要任务。它可以为许多下游任务提供有效的信息，例如避免碰撞，路径计划和静态地图构建。如何有效利用时空信息是3D激光雷达移动对象分割（LIDAR-MOS）的关键问题。在这项工作中，我们提出了一个新型的深神经网络，利用了时空信息和不同的LiDAR扫描表示方式，以提高LIDAR-MOS性能。具体而言，我们首先使用基于图像图像的双分支结构来分别处理可以从顺序的LiDAR扫描获得的空间和时间信息，然后使用运动引导的注意模块组合它们。我们还通过3D稀疏卷积使用点完善模块来融合LIDAR范围图像和点云表示的信息，并减少对象边界上的伪像。我们验证了我们提出的方法对Semantickitti的LiDAR-MOS基准的有效性。我们的方法在LiDar-Mos IOU方面大大优于最先进的方法。从设计的粗到精细体系结构中受益，我们的方法以传感器框架速率在线运行。我们方法的实现可作为开源可用：https：//github.com/haomo-ai/motionseg3d。

下一代网络将积极采用人工智能（AI）和机器学习（ML）技术，用于自动化网络和最佳网络操作策略。以Open Ran（O-Ran）为代表的新兴网络结构符合这一趋势，其规范中心的无线电智能控制器（RIC）用作ML应用程序主机。各种ML模型，尤其是强化学习（RL）模型，被认为是解决与RAN相关的多目标优化问题的关键。但是，应该认识到，当前大多数RL成功都局限于抽象和简化的仿真环境，这可能不会直接转化为复杂的真实环境中的高性能。主要原因之一是模拟与真实环境之间的建模差距，这可能会使RL代理通过模拟训练不适合真实环境。此问题称为SIM2REAL差距。本文在O-Ran的背景下引起了SIM2REAL挑战。具体而言，它强调了数字双胞胎（DT）可以作为模型开发和验证的地方的特征和好处。提出了几种用例，以举例说明并证明在真实环境中训练有训练的RL模型的故障模式。讨论了DT在协助RL算法开发方面的有效性。然后提出了通常用于克服SIM2REAL挑战的基于学习的基于艺术学习的方法。最后，从数据交互，环境瓶颈和算法设计等潜在问题的角度讨论了O-RAN中RL应用程序实现的开发和部署问题。

随着AI民主化的进展，机器学习（ML）已成功应用于边缘应用，如智能手机和自动驾驶。如今，更多的应用需要在具有极其有限的资源的微小设备上ML，如植入式心脏除颤器（ICD），其称为Tinym1。与边缘上的ML不同，有限的能量供应的Tinyml对低功率执行的需求较高。随机计算（SC）对数据表示的比特流是有价值的，因为它可以使用简单的逻辑门来执行基本的ML操作，而不是复杂的二进制加法器和乘法器。然而，由于算术单元的低数据精度和不准确性，SC通常遭受ML任务的低精度。增加现有作品中的比特流的长度可以减轻精度问题，但延迟较高。在这项工作中，我们提出了一种新的SC架构，即基于块的随机计算（BSC）。 BSC将输入划分为块，使得通过利用高数据并行性可以减少延迟。此外，提出了优化的算术单元和输出修订（我们）方案以提高精度。在它之上，设计了全局优化方法来确定块的数量，可以提高延迟功率折衷。实验结果表明，BSC可以优于现有的设计，以实现ML任务的高度超过10％，并且减少超过6倍。

We aim to bridge the gap between our common-sense few-sample human learning and large-data machine learning. We derive a theory of human-like few-shot learning from von-Neuman-Landauer's principle. modelling human learning is difficult as how people learn varies from one to another. Under commonly accepted definitions, we prove that all human or animal few-shot learning, and major models including Free Energy Principle and Bayesian Program Learning that model such learning, approximate our theory, under Church-Turing thesis. We find that deep generative model like variational autoencoder (VAE) can be used to approximate our theory and perform significantly better than baseline models including deep neural networks, for image recognition, low resource language processing, and character recognition.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.