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。