眼目光信息的收集为人类认知，健康和行为的许多关键方面提供了一个窗口。此外，许多神经科学研究补充了从眼睛跟踪中获得的行为信息，以及脑电图（EEG）提供的高时间分辨率和神经生理学标记。必不可少的眼睛跟踪软件处理步骤之一是将连续数据流的分割为与扫视，固定和眨眼等眼睛跟踪应用程序相关的事件。在这里，我们介绍了Detrtime，这是一个新颖的时间序列分割框架，该框架创建了不需要额外记录的眼睛跟踪模式并仅依靠脑电图数据的眼部事件检测器。我们的端到端基于深度学习的框架将计算机视觉的最新进展带到了脑电图数据的《时代》系列分割的最前沿。 Detr Time在各种眼睛追踪实验范式上实现眼部事件检测中的最新性能。除此之外，我们还提供了证据表明我们的模型在脑电图阶段分割的任务中很好地概括了。

Selecting an effective training signal for tasks in natural language processing is difficult: collecting expert annotations is expensive, and crowd-sourced annotations may not be reliable. At the same time, recent work in machine learning has demonstrated that learning from soft-labels acquired from crowd annotations can be effective, especially when there is distribution shift in the test set. However, the best method for acquiring these soft labels is inconsistent across tasks. This paper proposes new methods for acquiring soft-labels from crowd-annotations by aggregating the distributions produced by existing methods. In particular, we propose to find a distribution over classes by learning from multiple-views of crowd annotations via temperature scaling and finding the Jensen-Shannon centroid of their distributions. We demonstrate that using these aggregation methods leads to best or near-best performance across four NLP tasks on out-of-domain test sets, mitigating fluctuations in performance when using the constituent methods on their own. Additionally, these methods result in best or near-best uncertainty estimation across tasks. We argue that aggregating different views of crowd-annotations as soft-labels is an effective way to ensure performance which is as good or better than the best individual view, which is useful given the inconsistency in performance of the individual methods.

As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user's preferred answer ("sycophancy") and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.

Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI. Large neural models trained on such datasets produce impressive results and top many of today's benchmarks. A notable omission within this family of large-scale datasets is 3D data. Despite considerable interest and potential applications in 3D vision, datasets of high-fidelity 3D models continue to be mid-sized with limited diversity of object categories. Addressing this gap, we present Objaverse 1.0, a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category. We demonstrate the large potential of Objaverse via four diverse applications: training generative 3D models, improving tail category segmentation on the LVIS benchmark, training open-vocabulary object-navigation models for Embodied AI, and creating a new benchmark for robustness analysis of vision models. Objaverse can open new directions for research and enable new applications across the field of AI.

As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, and so we refer to the method as 'Constitutional AI'. The process involves both a supervised learning and a reinforcement learning phase. In the supervised phase we sample from an initial model, then generate self-critiques and revisions, and then finetune the original model on revised responses. In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences. We then train with RL using the preference model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a result we are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them. Both the SL and RL methods can leverage chain-of-thought style reasoning to improve the human-judged performance and transparency of AI decision making. These methods make it possible to control AI behavior more precisely and with far fewer human labels.

We introduce a sketch-and-solve approach to speed up the Peng-Wei semidefinite relaxation of k-means clustering. When the data is appropriately separated we identify the k-means optimal clustering. Otherwise, our approach provides a high-confidence lower bound on the optimal k-means value. This lower bound is data-driven; it does not make any assumption on the data nor how it is generated. We provide code and an extensive set of numerical experiments where we use this approach to certify approximate optimality of clustering solutions obtained by k-means++.

Developing safe and useful general-purpose AI systems will require us to make progress on scalable oversight: the problem of supervising systems that potentially outperform us on most skills relevant to the task at hand. Empirical work on this problem is not straightforward, since we do not yet have systems that broadly exceed our abilities. This paper discusses one of the major ways we think about this problem, with a focus on how to turn it into one that can be productively studied empirically. We first present an experimental design centered on choosing tasks for which human specialists succeed but unaided humans and current general AI systems fail. We then present a proof-of-concept experiment following meant to demonstrate a key feature of this experimental design and show its viability with two question-answering tasks: MMLU and time-limited QuALITY. On these tasks, we find that human participants who interact with an unreliable large-language-model dialog assistant through chat -- a trivial baseline strategy for scalable oversight -- substantially outperform both the model alone and their own unaided performance. These results are an encouraging sign that scalable oversight will be tractable to study with present models and bolster recent findings that large language models can productively assist humans with difficult tasks.

我们介绍了在Neurips'22接受的Chalearn Meta学习系列中的新挑战的设计和基线结果，重点是“跨域”元学习。元学习旨在利用从以前的任务中获得的经验，以有效地解决新任务（即具有更好的性能，较少的培训数据和/或适度的计算资源）。尽管该系列中的先前挑战集中在域内几乎没有学习问题，但目的是有效地学习n-way K-shot任务（即N级培训示例的N班级分类问题），这项竞赛挑战了参与者的解决方案。从各种领域（医疗保健，生态学，生物学，制造业等）提出的“任何通道”和“任何镜头”问题，他们是为了人道主义和社会影响而被选为。为此，我们创建了Meta-Album，这是来自10个域的40个图像分类数据集的元数据，从中，我们从中以任何数量的“方式”（在2-20范围内）和任何数量的“镜头”来解释任务”（在1-20范围内）。竞争是由代码提交的，在Codalab挑战平台上进行了完全盲目测试。获奖者的代码将是开源的，从而使自动化机器学习解决方案的部署可以在几个域中进行几次图像分类。

人工智能的最新趋势是将验证的模型用于语言和视觉任务，这些模型已经实现了非凡的表现，但也令人困惑。因此，以各种方式探索这些模型的能力对该领域至关重要。在本文中，我们探讨了模型的可靠性，在其中我们将可靠的模型定义为一个不仅可以实现强大的预测性能，而且在许多涉及不确定性（例如选择性预测，开放式设置识别）的决策任务上，在许多决策任务上表现出色，而且表现良好。强大的概括（例如，准确性和适当的评分规则，例如在分布数据集中和分发数据集上的对数可能性）和适应性（例如，主动学习，几乎没有射击不确定性）。我们设计了40个数据集的10种任务类型，以评估视觉和语言域上可靠性的不同方面。为了提高可靠性，我们分别开发了VIT-PLEX和T5-PLEX，分别针对视觉和语言方式扩展了大型模型。 PLEX极大地改善了跨可靠性任务的最先进，并简化了传统协议，因为它可以改善开箱即用的性能，并且不需要设计分数或为每个任务调整模型。我们演示了高达1B参数的模型尺寸的缩放效果，并预处理数据集大小最多4B示例。我们还展示了PLEX在具有挑战性的任务上的功能，包括零射门的开放式识别，主动学习和对话语言理解中的不确定性。

TensorFlow GNN（TF-GNN）是张量曲线的图形神经网络的可扩展库。它是从自下而上设计的，以支持当今信息生态系统中发生的丰富的异质图数据。Google的许多生产模型都使用TF-GNN，最近已作为开源项目发布。在本文中，我们描述了TF-GNN数据模型，其KERAS建模API以及相关功能，例如图形采样，分布式训练和加速器支持。