听力文本

This is Scientific American — 60-Second Science. I'm Christopher Intagliata.
When we humans talk to other humans, the sounds we make all have very specific meanings. "When I say apple you immediately imagine something that has the characteristics of an apple." Yossi Yovel, a neuroecologist at Tel Aviv University in Israel. "And the question is, do animals also have something like that?"
Yovel and his team chose to listen in on bats, which do a lot of vocalizing. In fact, in caves with vast numbers of bats, it's total cacophony. [bat cave] "It sounds like a crowd in a football stadium before the match has begun, or something like that." To simplify the problem, the researchers eavesdropped on a much smaller colony—just 22 Egyptian fruit bats.
Over several months, they recorded tens of thousands of calls, [call] along with synced-up video—which allowed them to decipher the speaker, the intended recipient, the situation, and the behavior resulting from each call. They then fed their huge database of calls to computers, to test whether machine learning could help make sense of them, using algorithms like the ones used for human speech recognition.

Turns out, the algorithms could correctly identify which bat made each call, more often than chance would predict. "And I can say to some extent who is this bat shouting at, so who is the addressee of this vocalization?" They could even figure out what a bat might be angry about, like "hey, stop sniffing me, or this is my food" and how the addressee might respond—meaning there's really quite a lot of information embedded in bat vocalizations. Pretty useful, if you live in the dark. The study is in the journal Scientific Reports.
As for whether we might someday have Google Translate for bat calls? "Well, Google, definitely not... but perhaps an iPhone." But seriously, "For sure in the next 100 years we will always be behind, we will never really be able to generate a bat to human dictionary. But definitely we will advance in that direction." And in doing so—it might shed a little light on animal communication in general. And how our own language evolved.
Thanks for listening for Scientific American — 60-Second Science Science. I'm Christopher Intagliata.

参考译文

这里是科学美国人——60秒科学JL%6j*x_xV。我是克里斯托弗·因塔利亚塔]uzl2K8MP[W3AmI,。
当我们人类互相交谈的时候，我们所发出的声音都具有特定的含义90zap[SN|~。“当我说苹果的时候，你马上就会想到苹果的特点^7SUtDiXjy&。”尤西·约维尔是以色列特拉维夫大学的神经生态学家e^jkc+OY~|UE^;FEB4R(。“问题是，动物有这样的能力吗？”
约维尔和他的团队选择了听蝙蝠的声音，因为蝙蝠会制造出很多声音uw!nnJXANZs9KE。实际上，在存在大量蝙蝠的洞穴里，声音总是那么不和谐(S7e*ORbCRq-=#^Z9。“那种声音听起来就像足球比赛开始前聚集在球场里的人群所发出的声音，或者其他类似的情况Fd&PcrRoE5O~)FmR&]。”为了简化问题，研究人员对一个较小的蝙蝠群进行了监听，这个群体只有22只埃及蝙蝠Wk]YRX4cfT;[8eQ_1~。
数个月以来，他们记录了数十万种蝙蝠的声音并录制了同步视频，这让研究者可以破译出发出声音的蝙蝠、接收声音的蝙蝠、当时的情景以及每个叫声所引发的行为c]i)UTX%vam)(M~。之后，研究人员将这个庞大的叫声数据库输入电脑，来测试机器学习是否可以理解这些叫声，是否可以利用分析人类言语认知的算法去理解蝙蝠叫声的含义KxQm,%MI^67v。
结果表明，这些算法可以正确识别出哪只蝙蝠发出了叫声，准确率高于预测KehH]P-9mFMB。“在某些程度上，我可以判断出这只蝙蝠在冲哪只蝙蝠叫喊，即这一声音的受体是谁vDgiDvcGHE6|2zt~。”他们甚至发现了蝙蝠会对哪些事感到生气，比如“喂，别再闻我了，这是我的食物”，他们还了解了声音的接受者可能会做出哪种反应，这表明蝙蝠的叫声中包含了大量的信息.jbidR,=%xbQY6。如果你生活在黑暗中，这非常有用QGRmYX1Xw;(JyDe。该研究结果发表在《科学报告》期刊上2BidX0Q;-E-IO。
那未来某一天我们是否可以用谷歌翻译蝙蝠的叫声呢？“谷歌，当然不行，不过也许苹果手机可以3V4g)1]t+-。”但是认真而言，“毫无疑问，在未来的100年，我们将一直处于落后地位，因为我们永远不能创造出蝙蝠人类词典vLenAk88fi@LQ+()r@。但是，我们肯定将向那个方向前进=mC7d9owut=iT%。”这样我们也许可以对动物之间的交流有个大致了解5d.*HC,6!Vng。同时也可以了解我们的语言是如何进化的Nc)if,!U6ma4KUy%YY。
谢谢大家收听科学美国人——60秒科学0MUDnE5rL3BB6B0(。我是克里斯托弗·因塔利亚塔]XQXt9vg=i;54。

译文为可可英语翻译，未经授权请勿转载！

重点讲解

重点讲解：
1. make sense of 理解；弄懂；
例句：I can't make sense of the message.
我看不懂这张字条k[e2.b44Al。
2. to some extent 在某种程度上；
例句：To some extent, I can understand their attitude.
我在某种程度上是能够理解他们的态度的E;aq^gGU+2Lgz%#。
3. for sure 无疑；肯定；
例句：One thing is for sure, it's not going to be easy.
有一点可以肯定，事情不会很容易ZFGm9HMHtD。
4. shed light on 使（某事）显得非常清楚；使人了解（某事）；
例句：Your statement does not shed light on the subject.
你的发言并没有说明问题H=INhOmMPn。