Scientists Are Trying to Teach AI How to Smell科学家尝试让人工智能获得嗅觉

Describing odors can be surprisingly complicated， even for a complex computer.

用文字来描述气味，即便是借助于复杂的计算机系统，也可能会出人意料地困难。

It’s hard to overstate the power of the nose—research says humans can distinguish more than a trillion odors. This is especially impressive when you remember that each individual odor is a chemical with a unique structure. Experts have been trying to discern patterns or logic in how chemical structure dictates smell， which would make it much easier to synthetically replicate scents or discover new ones. But that’s incredibly challenging—two very similarly structured chemicals could smell wildly different. When identifying smells is such a complicated task， scientists are asking： Can we get a computer to do it？

我们很难充分形容鼻子的功能有多么强大。研究显示，人类可以分辨超过一万亿种气味，这是一个惊人的数字，尤其是考虑到每种气味都是具有独特结构的化学物质。专家们一直在尝试找出化学结构决定气味的规律或逻辑，如此人工合成气味或发现新的气味便容易得多。但这极为困难，因为两种结构非常近似的化学物质，气味也可能截然不同。既然识别气味如此艰难，科学家便提出了这样的疑问：能否让电脑来完成这项任务？

Smell remains more mysterious to scientists than our senses of sight or hearing. While we can “map” what we see as a spectrum of light wavelengths， and what we hear as a range of sound waves with frequencies and amplitudes， we have no such understanding for smell. In new research， published in September 2023 in the journal Science， scientists trained a neural network with 5，000 compounds from two perfumery databases of odorants—molecules that have a smell—and corresponding smell labels like “fruity” or “cheesy.” The AI was then able to produce a “principal odor map” that visually showed the relationships between different smells. And when the researchers introduced their artificial intelligence to a new molecule， the program was able to descriptively predict what it would smell like.

相较于视觉或听觉，嗅觉对科学家来说更加神秘难解。我们可以把视觉“映射”为光谱，把听觉“映射”为具有频率和振幅的一系列声波，然而，气味却不能如此解读。在2023年9月的《科学》杂志上发表的最新研究中，科学家从两个香水呈香物质（散发气味的分子）数据库中提取了5000种化合物及相应的气味标签，如“果香”或“奶酪香”等，以此训练出一个神经网络。人工智能借此生成了一个“主气味图”，直观地展示出不同气味间的关系。当研究人员将新的分子输入人工智能系统时，该程序能够以文字描述的形式预测分子的气味。

The research team then asked a panel of 15 adults with different racial backgrounds living near Philadelphia to smell and describe that same odor. They found that “the neural network’s descriptions are better than the average panelist， most of the time，” says Alex Wiltschko， one of the authors of the new paper. Wiltschko is the CEO and co-founder of Osmo， a company whose mission is “to give computers a sense of smell” and that collaborated with researchers from Google and various US universities for this work.

研究团队随后请居住在费城周边、不同种族背景的15名成年人嗅闻同样的气味并进行描述。这篇新论文的作者之一亚历克斯·维尔奇科说，研究人员发现，“大多数情况下，神经网络给出的描述比普通小组成员的描述更加准确”。维尔奇科是欧斯莫公司的首席执行官及联合创始人，这家公司以“赋予计算机嗅觉”为使命，并为此与谷歌公司及多家美国大学的研究者开展合作。

“Smell is deeply personal，” says Sandeep Robert Datta， a neurobiology professor at Harvard University. （Datta has previously acted as a nominal advisor to Osmo， but was not involved in the new study.） And so， any research related to how we describe and label smells has to come with the caveat that our perception of smells， and how smells might relate to each other， is deeply entwined with our memories and culture. This makes it difficult to say what the “best” description of a smell even is， he explains. Despite all this， “there are common aspects of smell perception that are almost certainly driven by chemistry， and that’s what this map is capturing.”

哈佛大学神经生物学教授桑迪普·罗伯特·达塔说：“对气味的感受是因人而异的。”（达塔曾是欧斯莫公司的名义顾问，但未参与这项新研究。）所以，每一项与气味描述和气味标签相关的研究都必须明确：我们对气味的感知及气味间可能存在的相互关联，是与我们的记忆和文化息息相关的。达塔解释道，正因如此，很难说一种气味的“最佳”描述是什么。不过，“气味感知有一些共同点，基本确定是受化学成分的影响，这正是这张图要记录的内容”。

It’s important to note that this team is not the first or only to use computer models to investigate the relationship between chemistry and smell perception， Datta adds. There are other neural networks， and many other statistical models， that have been trained to match chemical structures with smells. But the fact that this new AI produced an odor map and was able to predict the smells of new molecules is significant， he says.

达塔补充道，需要指出的是，这个团队并不是第一个或唯一一个使用计算机模型研究化学成分与气味感知间关系的团队。还有其他神经网络及许多其他统计模型用于训练以实现化学结构与气味的匹配。不过，达塔表示，这款新型人工智能生成了气味图，并能预测新分子的气味，这一点具有重要意义。

This neural network strictly looks at chemical structure and smell， but that doesn’t really capture the complexity of the interactions between chemicals and our olfactory receptors， Anandasankar Ray， who studies olfaction at the University of California， Riverside， and was not involved in the research， writes in an email. In his work， Ray has predicted how compounds smell based on which of the approximately 400 human odorant receptors are activated. We know that odorant receptors react when chemicals attach to them， but scientists don’t know exactly what information these receptors transmit to the brain， or how the brain interprets these signals. It’s important to make predictive models while keeping biology in mind， he wrote.

加利福尼亚大学河滨分校的嗅觉研究专家阿南达桑卡尔·雷在邮件中写道，该神经网络把关注点全部放在化学结构和气味上，却未真正体现出化学物质与人类嗅觉受体间相互作用的复杂性。雷没有参与这项研究。在他的研究工作中，雷根据大约400个人体气味受体中哪个被激活，来预测化合物的气味。我们知道，当化学物质附着到气味受体上时，受体会做出反应，但科学家并不知道这些受体向大脑传递的具体信息，也不知道大脑是如何解读这些信号的。他写道，在创建预测模型时，必须考虑生物学因素。