由于少量转录的音频数据，为低资源语言开发自动语音识别（ASR）是一个挑战。对于许多这样的语言，音频和文本可单独使用，但没有带有抄录的音频。使用文本，可以通过文本到语音（TTS）系统综合生产语音。但是，许多低资源语言也没有质量的TTS系统。我们提出了一种替代方案：通过通过训练有素的TTS系统运行来自目标语言的文本来制作综合音频，用于高资源枢轴语言。我们研究了该技术在低资源环境中最有效的何时以及如何有效。在我们的实验中，使用数千种合成TTS文本语音对并复制真实数据来平衡可产生最佳结果。我们的发现表明，搜索一组候选枢轴语言可能会导致边际改进，令人惊讶的是，ASR性能可能会受到测量的TTS质量的提高而受到的伤害。这些发现的应用将ASR分别提高了64.5 \％和45.0 \％的字符误差率（CERR），分别对两种低资源语言：瓜兰\'i和suba。

Bibletts是一种在撒哈拉以南非洲使用的十种语言的大型，高质量的开放语音数据集。该语料库包含每语言最多86个小时的对齐，工作室质量的48kHz单扬声器唱片，从而能够开发高质量的文本到语音模型。代表的十种语言是：Akuapem Twi，Asante Twi，Chichewa，Ewe，Hausa，Kikuyu，Lingala，Luganda，Luganda，Luo和Yoruba。该语料库是由Biblica的Open.Bible Project制作和发行的圣经录音的衍生作品。我们已经对齐，清洁和过滤了原始录音，并还对每种语言的对齐子进行了手工检查。我们为具有Coqui TTS的文本到语音模型提供了结果。数据是根据商业友好的CC-SA许可发布的。

如果有足够的高质量数据和计算资源，现代语音合成技术可以产生自然的语音。但是，许多语言不容易获得此类数据。本文着重于低资源的非洲语言的语音综合，从语料库创建到共享和部署文本到语音（TTS）系统。我们首先为具有最低技术资源和主题专业知识的构建语音合成系统创建了一组通用说明。接下来，我们通过参与式方法从“发现”数据（现有记录）中创建新的数据集，并考虑可访问性，质量和广度。我们证明，即使在次优环境中记录下来，我们也可以开发出具有25分钟的语音的合成器，这些合成器即使在次优环境中记录下来。最后，我们发布了12种非洲语言的语音数据，代码和受过训练的声音，以支持研究人员和开发人员。

语言模型预训练的最新进展利用大规模数据集创建多语言模型。但是，这些数据集中大多遗漏了低资源语言。这主要是因为网络上没有很好地表示口语，因此被排除在用于创建数据集的大规模爬网中。此外，这些模型的下游用户仅限于最初选择用于预训练的语言的选择。这项工作调查了如何最佳利用现有的预培训模型来为16种非洲语言创建低资源翻译系统。我们关注两个问题：1）如何将预训练的模型用于初始预培训中未包含的语言？ 2）生成的翻译模型如何有效地转移到新域？为了回答这些问题，我们创建了一个新的非洲新闻语料库，涵盖16种语言，其中8种语言不属于任何现有评估数据集的一部分。我们证明，将两种语言转移到其他语言和其他领域的最有效策略是，以少量的高质量翻译数据微调大型预训练模型。

Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. We present our results comparing performance in terms of both accuracy and time-to-detection for multimodal data vs. a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.

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.

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.

System identification, also known as learning forward models, transfer functions, system dynamics, etc., has a long tradition both in science and engineering in different fields. Particularly, it is a recurring theme in Reinforcement Learning research, where forward models approximate the state transition function of a Markov Decision Process by learning a mapping function from current state and action to the next state. This problem is commonly defined as a Supervised Learning problem in a direct way. This common approach faces several difficulties due to the inherent complexities of the dynamics to learn, for example, delayed effects, high non-linearity, non-stationarity, partial observability and, more important, error accumulation when using bootstrapped predictions (predictions based on past predictions), over large time horizons. Here we explore the use of Reinforcement Learning in this problem. We elaborate on why and how this problem fits naturally and sound as a Reinforcement Learning problem, and present some experimental results that demonstrate RL is a promising technique to solve these kind of problems.

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer.github.io

Deep Reinforcement Learning (RL) agents are susceptible to adversarial noise in their observations that can mislead their policies and decrease their performance. However, an adversary may be interested not only in decreasing the reward, but also in modifying specific temporal logic properties of the policy. This paper presents a metric that measures the exact impact of adversarial attacks against such properties. We use this metric to craft optimal adversarial attacks. Furthermore, we introduce a model checking method that allows us to verify the robustness of RL policies against adversarial attacks. Our empirical analysis confirms (1) the quality of our metric to craft adversarial attacks against temporal logic properties, and (2) that we are able to concisely assess a system's robustness against attacks.