在信息推荐人中，由于需要处理知识域之间的语义和层次结构，因此出现了许多挑战。这项工作旨在提前建立一个国家感知的教育推荐系统，该制度包括知识主题之间的语义相关性，在语义相关主题中传播潜在信息。我们介绍了一种新的学习模型，可以利用Wikipedia链接图来利用知识组件之间的这种语义相关性，旨在更好地预测终身学习情景中的学习者参与和潜在知识。从这个意义上讲，语义Truelearn在利用贝叶斯机器学习时建立了一种人类直观的知识表示，以提高教育参与的预测性能。我们的大型数据集的实验表明，这种新的Truelearn算法的语义版本在预测性能方面实现了统计上显着的改进，简单的扩展为模型增加了语义意识。

据说教育中的人工智能（AI）有可能建立更多个性化的课程，以及全世界的民主化教育，并创造一个新的教学和学习方式。数百万学生已经开始受益于这些技术的使用，但世界各地的数百万不是。如果这一趋势持续，教育中的第一次交付可能会更大的教育不平等，以及目前的技术决定论叙事的全球教育资源的误操作。在本文中，我们专注于在教育中的AI未来造成猜测和构成问题，目的是开始按压对话，这些谈话将为技术渗透到技术的新一代教育方面。本文首先综合AI如何改变我们学习和教导的方式，专注于个性化学习伴侣的情况，然后举动讨论一些社会技术特征，这对于避免全球这些AI系统的危险至关重要（和也许确保他们的成功）。本文还讨论了使用自由，参与式和民主资源，如维基百科，开放教育资源和开源工具的潜力。我们还强调需要集体设计以人以人为本，透明，互动和协作的AI授权，并为利益攸关方提供完整的机构，以及支持新的新兴教育。最后，我们询问这一教育革命为自由主义和赋予教育权力，超出任何政治，文化，语言，地理和学习能力障碍而需要什么。

如果它使用所有可用数据同时学习预测器并通过在未见的数据上有效的统计证书证明其质量，则自我认证。最近的工作表明，通过优化PAC-Bayes界限训练的神经网络模型不仅可以提高准确的预测因子，而且涉及严格的风险证书，致力于实现自我认证的学习。在这种情况下，由于能够利用所有数据来学习后验并同时证明其风险，基于PAC-Bayes界的学习和认证策略尤其吸引力。在本文中，我们评估了Pac-Bayes灵感目标的概率神经网络中自我认证的进展。我们经验比较（在4个分类数据集）的经典测试集合，用于确定性预测器的界限和用于随机自我认证的预测器的PAC-Bayes。我们首先表明这两个概括界界不太远非出现超出样本测试集错误。然后我们认为，在数据饥饿制度中，保持测试集合的数据对泛化性能产生不利影响，而基于PAC-Bayes界限的自我认证策略不会遭受这种缺点，证明它们可能是适当的选择小数据制度。我们还发现Pac-Bayes的概率神经网络受到Pac-Bayes启发目标的启发目标导致证书可以令人惊讶地竞争常用的测试集合。

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.

In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short). This class of systems is widely used in control engineering and econometrics, in particular, they represent a special case of recurrent neural networks. In this paper we 1) formalize the learning problem for stochastic LTI systems with inputs, 2) derive a PAC-Bayesian-Like error bound for such systems, 3) discuss various consequences of this error bound.

We demonstrate how efficient autonomous drone swarms can be in detecting and tracking occluded targets in densely forested areas, such as lost people during search and rescue missions. Exploration and optimization of local viewing conditions, such as occlusion density and target view obliqueness, provide much faster and much more reliable results than previous, blind sampling strategies that are based on pre-defined waypoints. An adapted real-time particle swarm optimization and a new objective function are presented that are able to deal with dynamic and highly random through-foliage conditions. Synthetic aperture sensing is our fundamental sampling principle, and drone swarms are employed to approximate the optical signals of extremely wide and adaptable airborne lenses.

Generative AI has matured to a point where large-scale models can generate text that seems indistinguishable from human-written text and remarkably photorealistic images. Automatically measuring how close the distribution of generated data is to the target real data distribution is a key step in diagnosing existing models and developing better models. We present MAUVE, a family of comparison measures between pairs of distributions such as those encountered in the generative modeling of text or images. These scores are statistical summaries of divergence frontiers capturing two types of errors in generative modeling. We explore four approaches to statistically estimate these scores: vector quantization, non-parametric estimation, classifier-based estimation, and parametric Gaussian approximations. We provide statistical bounds for the vector quantization approach. Empirically, we find that the proposed scores paired with a range of $f$-divergences and statistical estimation methods can quantify the gaps between the distributions of human-written text and those of modern neural language models by correlating with human judgments and identifying known properties of the generated texts. We conclude the paper by demonstrating its applications to other AI domains and discussing practical recommendations.

Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.