Detecting loneliness

We know that artificial intelligence (AI) is smart enough to do a few things our minds cannot, and with incredible accuracy. And now, it seems it also has the capacity to detect loneliness in humans, which is an otherwise challenging task.
我们知道，人工智能足够聪明，能以极高的精确度实现人类大脑做不到的一些事。而如今，人工智能似乎还能做到又一项极具挑战的任务 —— 检测出人类的孤独指数。

A new study, led by researchers at the University of California San Diego School of Medicine, US, has shown how AI tools can predict levels of loneliness from a person’s speech with an accuracy rate of 94 percent.
美国加利福尼亚大学圣迭戈分校研究人员发起了一项新研究，展现了人工智能工具如何通过一个人的语言来预测其孤独程度，准确率高达94%。

The study focused on 80 participants aged 66 to 94, a population particularly vulnerable to loneliness. The subjects were asked 20 questions from the University of California Los Angeles (UCLA) Loneliness Scale, which uses a four-point rating scale for questions such as “How often do you feel left out?” and “How often do you feel part of a group of friends?”
该研究聚焦66-94岁之间的80名测试者，这一人群尤其容易感到孤独。加州大学洛杉矶分校的孤独感量表准备了20个问题，要求测试者作答，量表中使用了一个四点评分量表对一些问题进行回答，例如：“你经常感觉被冷落？”“你经常感觉到自己是朋友中的一员吗？”

They were also interviewed in private conversations, which were recorded and transcribed by researchers. The transcripts were then examined using natural language processing tools, including IBM Watson Natural Language Understanding (WNLU) software, to quantify expressed emotions.
测试者也接受了私人谈话式的采访，这些谈话被研究人员录音并记录。记录文本则使用包括IBM沃森自然语言理解软件（WNLU）在内的自然语言处理工具进行检验，从而量化这些表达的情绪。

The interesting thing about this system is that it not only uses dictionary-based methods, such as searching for specific words that express fear, but also presents corresponding patterns by testing the words used in the response.
这一系统的有趣之处在于其不光使用了基于词典的方法，如检索表达恐惧的特定词汇，还能通过测试受试者回应中的用词体现相应模式。

Varsha Badal, the first author of the study, noted that the WNLU software system uses deep learning to extract data from keywords, categories, emotions and grammar.
该研究的第一作者瓦尔沙·巴达尔称，WNLU软件系统使用深度学习，能从关键词、类别、情绪、语法中提取数据。

“Natural language processing and machine learning can systematically examine long interviews from multiple individuals and explore how subtle speech features such as emotions may indicate loneliness,” Badal said. “Similar emotion analyses by humans would be open to bias, lack consistency, and require extensive training to standardize.”
“自然语言处理和机器学习能系统地检查来自多位测试者的长时间访谈，并探索情感等微妙的语言特征是如何表达孤独感的，”巴达尔表示。“人类进行类似的情绪分析或许会存在偏见，缺乏一致性，并需要大量的标准化训练。”

The more lonely a person felt, the longer their responses to direct questions regarding loneliness. The system was capable of not just detecting the degree of loneliness in each subject, but also showing differences between the way men and women spoke about loneliness. The men were found to use more fearful and joyful words in their responses, while the women tended to acknowledge feeling lonely during interviews.
一个人越感到孤独，对于有关孤独的问题回答也越长。这一系统不光能检测到每个话题中的孤独程度，还能体现男女在谈及孤独时的不同表达方式。研究发现，男性会在回答中使用更多与恐惧、喜悦相关的词汇，而女性则是会在采访中承认感到孤独。

Co-author Dilip Jeste said that the IBM-UC San Diego Center is now exploring natural language patterns of loneliness and wisdom, which are inversely linked in older adults. “Speech data can be combined with our other assessments of cognition, mobility, sleep, physical activity and mental health to improve understanding of aging and to help contribute to successful aging,” he said.
联合作者迪利普·杰斯特表示，IBM-加州大学圣迭戈分校中心正在探索孤独和智慧的自然语言模式特征，这些特征在老年人群中呈现负关联。“语言数据能与我们对于认知、运动、睡眠、生理活动以及心理健康的其他评估相结合，从而增强我们对衰老的理解认知，并有助于我们成功度过老年生活，”他说道。

以上文章内容选自《21世纪学生英文报高中版》，详情请见《21世纪学生英文报高中版》高二818期