在许多行业中，客户流失预测是一项宝贵的任务。在电信中，鉴于数据的高维度以及确定潜在的挫败感签名是多么困难，这可能代表了关于未来流失行为的重要驱动因素。在这里，我们提出了一个新颖的贝叶斯分层联合模型，该模型能够根据不同电视观看旅程中发生的事件以及事件之间需要多长时间来表征客户资料。该模型大幅度地将数据的维度从每个客户的数千个观察值降低到11个客户级参数估计和随机效果。我们使用来自40个BT客户（有20名活跃和20名最终取消订阅的20人）的数据测试我们的方法，他们的电视观看行为是从2019年10月到2019年12月的，总计约为半百万。使用贝叶斯分层模型的参数估计和随机效应采用不同的机器学习技术，作为在验证中与100 \％真实的正率和14 \％的假正率相关的最高92 \％精度可预测流失的精度放。我们提出的方法是降低数据维度的有效方法，同时保持了高描述性和预测能力。我们提供代码以在https://github.com/rafamoral/profiling_tv_watching_behaviour上实现贝叶斯模型。

在植物繁殖中，环境（GXE）相互作用的基因型存在对耕作决策和引入新作物品种的影响很大。线性和双线性项的组合已被证明在建模这种类型的数据方面非常有用。识别GXE的一种广泛使用的方法是加性主要效应和乘法交互作用（AMMI）模型。但是，由于数据经常可能是高维的，马尔可夫链蒙特卡洛（MCMC）方法在计算上可能是不可行的。在本文中，我们考虑了这种模型的变异推理方法。我们得出用于估计参数的变异近似值，并使用模拟和真实数据将近似值与MCMC进行比较。我们提出的新推论框架平均要快两倍，同时保持与MCMC相同的预测性能。

Morality in dialogue systems has raised great attention in research recently. A moral dialogue system could better connect users and enhance conversation engagement by gaining users' trust. In this paper, we propose a framework, MoralDial to train and evaluate moral dialogue systems. In our framework, we first explore the communication mechanisms of morality and resolve expressed morality into four sub-modules. The sub-modules indicate the roadmap for building a moral dialogue system. Based on that, we design a simple yet effective method: constructing moral discussions from Rules of Thumb (RoTs) between simulated specific users and the dialogue system. The constructed discussion consists of expressing, explaining, and revising the moral views in dialogue exchanges, which makes conversational models learn morality well in a natural manner. Furthermore, we propose a novel evaluation method in the framework. We evaluate the multiple aspects of morality by judging the relation between dialogue responses and RoTs in discussions, where the multifaceted nature of morality is particularly considered. Automatic and manual experiments demonstrate that our framework is promising to train and evaluate moral dialogue systems.

Drawing from the resources of psychoanalysis and critical media studies, in this paper we develop an analysis of Large Language Models (LLMs) as automated subjects. We argue the intentional fictional projection of subjectivity onto LLMs can yield an alternate frame through which AI behaviour, including its productions of bias and harm, can be analysed. First, we introduce language models, discuss their significance and risks, and outline our case for interpreting model design and outputs with support from psychoanalytic concepts. We trace a brief history of language models, culminating with the releases, in 2022, of systems that realise state-of-the-art natural language processing performance. We engage with one such system, OpenAI's InstructGPT, as a case study, detailing the layers of its construction and conducting exploratory and semi-structured interviews with chatbots. These interviews probe the model's moral imperatives to be helpful, truthful and harmless by design. The model acts, we argue, as the condensation of often competing social desires, articulated through the internet and harvested into training data, which must then be regulated and repressed. This foundational structure can however be redirected via prompting, so that the model comes to identify with, and transfer, its commitments to the immediate human subject before it. In turn, these automated productions of language can lead to the human subject projecting agency upon the model, effecting occasionally further forms of countertransference. We conclude that critical media methods and psychoanalytic theory together offer a productive frame for grasping the powerful new capacities of AI-driven language systems.

Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static and homogeneous across individuals, so that aligning to a a single "generic" user will confer more general alignment. Here, we embrace the heterogeneity of human preferences to consider a different challenge: how might a machine help people with diverse views find agreement? We fine-tune a 70 billion parameter LLM to generate statements that maximize the expected approval for a group of people with potentially diverse opinions. Human participants provide written opinions on thousands of questions touching on moral and political issues (e.g., "should we raise taxes on the rich?"), and rate the LLM's generated candidate consensus statements for agreement and quality. A reward model is then trained to predict individual preferences, enabling it to quantify and rank consensus statements in terms of their appeal to the overall group, defined according to different aggregation (social welfare) functions. The model produces consensus statements that are preferred by human users over those from prompted LLMs (>70%) and significantly outperforms a tight fine-tuned baseline that lacks the final ranking step. Further, our best model's consensus statements are preferred over the best human-generated opinions (>65%). We find that when we silently constructed consensus statements from only a subset of group members, those who were excluded were more likely to dissent, revealing the sensitivity of the consensus to individual contributions. These results highlight the potential to use LLMs to help groups of humans align their values with one another.

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.

量化在用户生成的文本，新闻或公共话语中表达的道德叙事对于理解个人的关注点和观点并防止暴力抗议和社会两极分化至关重要。发展道德基础理论（MFT）是为了在五维规模系统中运行道德。该理论的最新发展敦促引入新的基础，即自由基金会。直到最近才添加到理论中，没有可用的语言资源来评估文本语料库中是否存在自由。鉴于它对当前的社会问题（例如疫苗接种辩论）的重要性，我们提出了两种数据驱动的方法，这些方法是根据来自具有不同世界观的在线新闻来源的校准文档生成的两个候选词典。经过广泛的实验，我们为研究界做出了贡献，这是一个新颖的词典，它以对比观点对比的个人通过书面文本表达自己的方式来评估自由道德基础。 LibertyMFD词典可以成为政策制定者了解有争议的社会问题（例如疫苗接种，堕胎甚至起义）的各种观点的宝贵工具，并且大规模发生。

机器学习应用在我们的社会中变得越来越普遍。由于这些决策系统依赖于数据驱动的学习，因此风险是它们会系统地传播嵌入数据中的偏见。在本文中，我们建议通过引入一个框架来生成具有特定类型偏差及其组合的综合数据的框架来分析偏见。我们深入研究了这些偏见的性质，讨论了它们与道德和正义框架的关系。最后，我们利用我们提出的合成数据生成器在不同的情况下进行不同的偏置组合进行实验。因此，我们分析了偏见对未经降低和缓解机器学习模型的性能和公平度量的影响。

如果未来的AI系统在新的情况下是可靠的安全性，那么他们将需要纳入指导它们的一般原则，以便强烈地认识到哪些结果和行为将是有害的。这样的原则可能需要得到约束力的监管制度的支持，该法规需要广泛接受的基本原则。它们还应该足够具体用于技术实施。本文从法律中汲取灵感，解释了负面的人权如何履行此类原则的作用，并为国际监管制度以及为未来的AI系统建立技术安全限制的基础。

当我们授予人工智能在医疗保健，警务和驾驶等环境中增加权力和独立性时，AI面临道德困境，但缺乏解决这些困境的工具。监管机构，哲学家和计算机科学家对不道德人工智能的危险的警告促使人们对自动化道德的兴趣，即可以执行道德推理的机器的发展。但是，自动化伦理学的先前工作很少与哲学文学相关。哲学家花了几个世纪的时间来辩论道德困境，因此自动化道德在借鉴哲学文学时将是最细微，一致和可靠的。在本文中，我提出了对康德哲学传统忠于自动化的康德伦理的实施。我正式化了康德在二元义逻辑中的绝对命令，在Isabelle定理供摊位中实施了这种形式化，并开发了一个测试框架，以评估我的实施与康德伦理的预期特性相一致。我的系统是朝着哲学上成熟的道德AI代理商迈出的早期一步，它可以在复杂的道德困境中划分判断，因为它基于哲学文学。因为我使用了交互式定理供您，所以我的系统的判断是可以解释的。