考研英语阅读理解外刊原文经济学人

1、Al Predicts Pig Emotions From Grunts and Squeals人工智能可以通过咕噜声和尖叫声来预测猪的情绪Researchers are using artificial intelligence (Al) as a non-invasive tool in efforts to gain insights into the emotions of animals based on the sounds they make. In a new study published in Scientific Reports this week, a global t

2、eam of researchers created an Al machine learning algorithm to help decode the emotional state of pigs based on their vocalizations.研究人员正在使用人工智能(Al)作为一种非侵入性工具，试图根据动物发 出的声音来洞察它们的情绪。在本周发表在科学报告(Scientific Reports)上 的一项新研究中，一个全球研究团队创立了一种人工智能机器学习算法，以根据 猪的发声来帮助解码它们的情绪状态。u Vocal expression of emotions has

3、been observed across species and could provide a non-invasive and reliable means to assess animal emotions/ wrote the study authors affiliated with Harvard University in the United States and other research institutions based in Switzerland, Denmark, the Czech Republic, Germany, Italy, Norway, and F

4、rance.美国哈佛大学(Harvard University)和瑞士、丹麦、捷克共和国、德国、意 大利、挪威和法国其他研究机构的研究作者写道：“在不同物种中都观察到了情 绪的声音表达，这可以为评估动物情绪提供一种非侵入性和可靠的方法。”According to the American Psychological Association (APA), emotions are complex reaction patterns that include behavioral, physiological, and experiential elements. The APA distingu

5、ishes between emotions and feelings, which are not interchangeable terms. Feelings are self-contained phenomenal experiences/ whereas emotions typically involves feeling but differs from feeling in having an overt or implicit engagement with the world/根据美国心理协会(American Psychological Association)的说法，

6、情绪是复杂 的反响模式，包括行为、生理和经验因素。美国心理协会区分了情绪和感觉，它 们是不可互换的术语。感觉是“独立的现象体验”，而情感“通常涉及感觉，但 与感觉不同的是，它与世界有着公开或隐性的接触”。“ Due to the impact of emotions on vocalization, the analysis of vocal expression of emotions is increasingly being considered as an important non-invasive tool to assess the affective aspects of an

7、imal welfare/ the researchers wrote.研究人员写道：“由于情绪对发声的影响，对情绪的声音表达的分析越来越 被认为是评估动物福利情感方面的重要非侵入性工具。”For this study, the researchers analyzed a dataset of commercial pig vocalizations with over 7,400 calls produced by over 400 pigs in 19 context categories from “birth to slaughter/ The pig vocalizations i

8、ncluded high-frequency calls that are common in negative contexts such as screams and squeals and low-frequency calls such as grunts that often occur in neutral or positive situations.在这项研究中，研究人员分析了一个商业猪叫声数据集，共有900头猪在19 个情境类别一一从“出生到屠宰”一一中发出7400多个叫声。猪的叫声包括在 消极环境中常见的高频叫声，如大叫和尖叫，以及在中性或积极环境中常见的低 频叫声，如咕噜

9、声。The positive context categories include huddling, before and after nursing, enriched, reunion, running to and from an arena, and positive conditioning. The negative context categories are missed nursing, surprise, handling and waiting in a slaughterhouse, barren, novel object, negative conditionin

10、g, fighting, crushing, physical restrain/holding, castration without anesthetics, and isolation.积极的情境类别包括挤成一团、护理前后、充裕、团聚、跑进跑出竞技场以 及积极的条件反射。消极的情境类别包括错过护理、惊讶、在屠宰场处理和等待、 贫瘠、新奇物体、消极的条件反射、打架、挤压、身体约束/控制、无麻醉剂的阉 割和隔离。The researchers discovered that the pig grunts are shorter in positive situations, high-pit

11、ched squeals happen more frequently in negative situations, and low-pitched grunts are prevalent in positive and negative contexts.研究人员发现，猪在积极的情况下发出的咕噜声更短，在消极的情况下，高 音调的尖叫声更频繁，而低音调的咕噜声在积极和消极的情况下普遍存在。The scientists tested two ways of classifying the pig vocalizationsa permuted discriminant function an

12、alysis (pDFA) and an Al image classifying neural network, specifically a convolutional neural network (CNN) that uses spectrograms of the vocalization.科学家们测试了两种对猪发声进行分类的方法一一排列判别函数分析 (pDFA)和人工智能图像分类神经网络，特别是使用发声频谱图的卷积神经网络 (CNN)oThey found that the Al neural network classified the valence (positive or

13、negative) with 91.5 percent accuracy, outperforming the pDFA analysis. The researchers credit the Al neural networks ability to make more accurate predictions on its ability to preserve more encoded information from the spectrograms of entire vocalizations.他们发现，人工智能神经网络对价态(正或负)的分类准确率为9L5%,优 于排列判别函数分析。研究人员相信，人工智能神经网络能够对其从