Squiboon

ISCF健康老龄化后续基金2023是健康老龄化催化剂奖计划的一部分，由UKRI健康老龄化挑战资助，并与美国国家医学院（NAM）健康长寿全球大挑战合作。在Zinc的项目支持下，后续资金将用于建立之前的健康老龄化催化剂奖资金，目的是将在护理环境中使用对话式人工智能来解决孤立和孤独问题的研究商业化。近年来，大型语言模型（如GPT-3）的使用取得了很大进展，这些模型现在正在渗入主流，并导致人类与计算机之间交互的根本转变。然而，如果这些进步对老龄化人口有益，还需要进一步的研究。除了那些视力、听力、记忆或其他损伤恶化的人的可访问性问题外，预训练模型的偏差和缺陷也带来了新的挑战。我们将探索新颖和包容的交流方式，使用可访问性的最佳实践，使用简单的图形界面结合自然的对话音频。我们将在小组环境中测试会话AI，探索老年人的语音特性，声音质量如何影响语音到文本转换的错误率，以及语音识别模型区分说话者的能力（diarisation）。我们将研究对话中的情感内容，并测试计算机追踪小组讨论的情感弧线的能力。最后，我们将收集新的音频、视觉和文本数据集，以定制训练人工智能，使其更具包容性和代表性。这些见解都将用于支持额外的产品开发，这些产品是在之前的催化剂奖之后，由我们的老年人指导小组进行的迭代设计过程中产生的。该产品将在退休社区和养老院部署，以探索该产品在解决社区孤独感方面的有效性，并确定和解决实施挑战。

The ISCF Healthy Ageing Follow on Fund 2023 is part of the Healthy Ageing Catalyst Award programme, funded by the UKRI Healthy Ageing Challenge and in collaboration with the US National Academy of Medicine's (NAM) Healthy Longevity Global Grand Challenge. With Zinc's programme of support, the Follow on Funding will be used to build upon previous Healthy Ageing Catalyst Award funding, with the aim of commercialising research in the use of conversational AI in care settings to tackle isolation and loneliness.Much progress has been made in recent years with the use of Large Language Models, such as GPT-3, and these are now seeping into the mainstream and leading to a fundamental shift in the interaction between humans and computers.However, further research is necessary if these advances are to be of benefit to the ageing population. In addition to the accessibility issues of those with deteriorating eyesight, hearing, memory, or other impairments, there are new challenges resulting from biases and deficiencies in the pre-trained models. We'll be exploring novel and inclusive ways of communicating, using best practices in accessibility, using simple graphical interfaces in combination with natural, conversational audio.We'll be testing conversational AI in group settings, exploring how speech idiosyncrasies of older adults, sound quality affect error rates in speech-to-text conversion, and the ability of speech recognition models to tell the speakers apart (diarisation).We'll be looking at the emotional content of conversations and testing computers' abilities to track the emotional arc of a group discussion.Finally, we'll be gathering new audio, visual and textual datasets to custom-train the artificial intelligence to be more inclusive and representative of older adults.These insights will all be used to underpin additional product development that has arisen from an iterative design process with our steering group of older adults, following the previous Catalyst Award. This product will be deployed across retirement communities and care homes, to explore the effectiveness of the product in tackling loneliness in communities, as well as to identify and address implementation challenges.