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人工智能是否能创造素食世界?

更新时间:2018-1-11 21:46:49 来源:纽约时报中文网 作者:佚名

Could AI help create a meat-free world?
人工智能是否能创造素食世界?

Remember the last burger you really enjoyed – try to summon up its rich, juicy taste in your mind and its chewy, firm-yet-soft-yet-crunchy texture. Try to recall how the taste filled your mouth with flavour as you bit into it. Remember the smell. Remember how satisfying it was.

回想一下你最近吃过的美味汉堡——试着在脑海里重现它丰厚多汁的味道和富有嚼劲、坚实、柔软又脆爽的口感。试着回忆汉堡塞到嘴里时的风味和香气。记住它给你带来的满足感。

Now think about how it might have tasted without any meat in it. Farming the meat for beef burgers takes a hefty toll on the environment around the world. But would you have been happy with the spongy substitute some vegetarians enjoy? What if there was another way of recreating the sensory extravaganza of a burger?

现在设想一下假如那个汉堡里面没有肉,味道会变成怎样。制作牛肉汉堡需要畜牧业的支持,而这对全世界的环境都造成了巨大的压力和破坏。如果我们使用一些素食主义者喜欢的替代品,你会满意吗?假如我们能用别的方法重现汉堡的味觉盛宴,那会如何?

A group of entrepreneurs are now turning to artificial intelligence to find the answer. They want to produce something so similar in taste and texture to a real beef burger that it would be impossible to tell if animals were involved in its production.

现在,正有一批企业家试图通过人工智能寻找答案。他们希望制造出在味道和口感上和真实的牛肉汉堡类似的东西,以至于人们将无法判断制造过程中是否使用了动物制品。

Meat is not their only target: mayonnaise, cookie dough, cheese, chocolate, and pretty much every other food produced using animal-based ingredients are in their sights. Their dream is to make the world’s diet vegan by default, to make a plant-based option the easiest, cheapest and most convenient one on the menu.

肉类并非他们唯一的目标:蛋黄酱、曲奇饼、奶酪、巧克力,以及其他所有使用动物原料生产的食品都在他们的视线范围之内。他们的梦想是让素食成为每个人最简单、最便宜、最方便的选择,这样一来全球人口都能吃素。

Of course, the idea of replacing animal-based food is not new. But AI is offering a more powerful and promising way of doing this. It is allowing food scientists to explore new ingredients, to develop surprising recipes, and to find innovative ways of replicating all the tasty fats and proteins that eggs, milk, and meat bring to our food.

当然,取代动物性食物的想法并不新鲜,但人工智能提供了一种更强大、更有前途的方法。食品科学家可以依靠它探索新的食材,开发令人惊讶的食谱,寻找创新的方法来重现鸡蛋、牛奶和肉类中脂肪和蛋白质给食物带来的美味。

Crazy food

疯狂的食物

“The way we eat today is, mostly, crazy,” says Josh Tetrick, the founder and CEO of food start-up Hampton Creek, who are among those using AI to develop new foods. “Six billion people are just eating really bad food.”

食品初创企业Hampton Creek的创始人兼首席执行官乔希·蒂特里克(Josh Tetrick)正在利用人工智能开发新食品。他说:"人们目前的饮食方式简直疯狂,60亿人都在吃着非常差劲的食物。"

Despite being a strict vegan who would prefer a kale salad rather than a muffin, Tetrick is convinced that today a “healthy and sustainable food only works for a tiny slice of the population”. He imagines a future where choosing to be vegetarian or vegan is not something only open to the better off in society. He wants to reach those who don’t get to choose.

尽管严格的素食主义者更喜欢甘蓝沙拉而不是松饼,但蒂特里克坚信,如今"健康和可持续的食物只适用于一小部分人"。他想象中的未来是这样的:选择素食或成为严格的素食主义者不只是富裕阶层的选择。他想要触及那些没有选择机会的人。

His quest started in a very unsophisticated fashion – he just scouted for plant-based food, adding them to a basic database. “I had no idea of what machine learning was,” he says. “I had no idea of what computational biology was.”

蒂特里克一开始的探索方法一点都不复杂——他只是寻找植物性食物,并将它们添加到基本的数据库中。他说:"那时,我还不知道什么是机器学习,也不知道什么是计算生物学。"

Then he was introduced to AI by a friend. The powerful machine learning algorithms allow him to systematically find new ingredients or formulations that can provide substitutes for animal-based products.

后来,有朋友向他介绍了人工智能。强大的机器学习算法可以帮助他系统性地寻找新的食材或配方,以替代动物性产品。

He is not alone in his mission.Thousands of miles south, in Santiago, Chile, Matias Muchnick, Karim Pichara and Pablo Zamora are trying something similar with their new company NotCo. They want people to eat in a more nutritious and less environmentally taxing way.

蒂特里克并不是唯一一个在从事这项工作的人。在智利的圣地亚哥,卡利姆·皮查拉(Karim Pichara)、马蒂亚斯·穆奇尼克(Matias Muchnick)和帕布罗·萨莫拉(Pablo Zamora)创立了NotCo公司,开展类似的探索。他们希望人们以更有营养、更环保的方式享受美食。

“If we had to deliberately come out with the worst possible way to feed ourselves, it would be the way we do it today,” says Muchnick.

穆奇尼克表示:"如果我们必须故意用最糟糕的方法来喂养自己,那就是我们目前的饮食方式了。"

Animal-based food takes a hefty toll on our planet’s resources. As outlined in this BBC Future article, eliminating meat from the human diet would cut up to 60% of the food-related greenhouse emissions, and free up the disproportionate share of fresh water and agricultural land that livestock use. There also are the many ethical, labour, land and garbage disposal issues around big meat processing plants.

动物性食物给地球资源带来了巨大损失和破坏。正如BBC Future的文章所述,从人类饮食中去除肉类最多可以减少60%与食物有关的温室气体排放,并节约养殖业所需的大量淡水和农业用地资源。另外,大型肉类加工厂还存在很多伦理问题、劳动力问题、土地和垃圾处理问题。

“The human costs are tremendous, these companies essentially starve the people who grow the meat for them,” says the journalist Katy Keiffer, author of the book What’s the Matter with Meat.

《肉的问题》(What's the Matter with Meat)一书作者、记者凯蒂·基弗(Katy Keiffer)说:"因为人力成本极高,这些畜牧企业几乎让其从业者陷入贫穷困境。"

Still, meat demand in the world is increasing as populations and economies grow. Global production of meat has doubled from 159 million tonnes in 1986 to almost 318 million in 2014, according to the UN’s Food and Agriculture Organisation (FAO) . Even in countries where it is not a luxury, meat consumption stubbornly refuses to fall. Both in the US and in the UK, it is estimated that the proportion of the population who are vegetarians – let alone vegans – is in the single digits.

尽管如此,随着人口和经济的增长,世界上的肉类需求依然在增加。联合国粮农组织(FAO)的数据显示,全球肉类产量从1986年的1.59亿吨增加到2014年的近3.18亿吨,翻了一番。即使在肉食不被认为是很奢侈的食物的国家,肉类消费也居高不下。据估计,在美国和英国,素食者(更不用说严格素食者)占总人口的比例仅为个位数百分比。

As Keiffer says, “it's going to be hard to tell people who did not have the advantage of eating meat and are just beginning to discover it, that they can't have it”. So, whatever replacements for meat that Tetrick and Muchnick come up need to taste and feel like the original product. But they also have to be scaleable, accessible, and hopefully, healthier. So how is AI helping them to do this?

正如基弗所说:"很难告诉那些以前没有吃肉条件、现在刚开始吃肉的人:他们不应该吃肉。"所以,不管蒂特里克和穆奇尼克用什么代替肉,味道和口感必须像真的肉。同时,这些替代品也必须是可扩大规模的,易于获得的,而且最好更加有益健康。那么人工智能如何帮助实现这个目标呢?

Building blocks

基础

For people like Tetrick and Muchnick, the way to start is a change in perspective. Their idea of a muffin is very different from how the average person sees one. They see a toolbox where we see a pantry; they see a chemical experiment where we see a treat. “The muffin needs to aerate, it needs to bun, it needs to brown. It needs a texture, it needs to have a shelf life”, explains Tetrick. (He offers no word on how tasty it needs to be, however.)

对于像蒂特里克和穆奇尼克这样的人来说,可以从改变看法开始。他们对松饼的看法与一般人大不相同。在他们眼中,小厨房就是工具箱;别人请客,他们当作是一次化学实验。蒂特里克解释说:"松饼需要膨胀起来,变成棕色,要有质地,还要一定的保质期。"然而,他并没有提到松饼应有的美味程度。

Their aim is to find a way to make the muffin do all this, but using different ingredients. It is a “very difficult puzzle” to solve, says Ricardo San Martin, visiting professor at the Alternative Meat Lab of the University of California in Berkeley. On the one hand, every single aspect of the experience, from the taste and texture to the way the food changes when it is heated, is the product of specific molecules or a micro-component, like proteins or fats. In our current diet, many of these come from animal ingredients.

他们的目标是找到一种方法让松饼满足这些条件,但使用不同的原料。加州大学伯克利分校的另类肉类实验室(Alternative Meat Lab)客座教授里卡多·圣·马丁(Ricardo San Martin)说,这个问题"很难解决"。一方面,从口感和质地到食物加热时的变化,饮食体验的每个方面都是特定分子或微成分的产物,比如蛋白质或脂肪。在我们目前的饮食中,很多都来自于动物性原料。

The first step to finding replacements is identifying as many candidates as possible. This is done by scouting the world in search of edible plants. The thing is, no one knows exactly which ones would work. Even the people who eat them every day might have no inkling they could be used to replace pork, or eggs.

寻找肉类替代品的第一步是确定尽可能多的候选食材,这可以通过在世界各地搜寻食用植物来完成。问题是,没有人确切知道哪些会成功。即使是每天吃这些食物的人,也不知道它们有可能代替猪肉或鸡蛋。

Then the food has to be analysed. Researchers have to figure out what each plant-based ingredient is made of, right down to the molecular level, as well as the proportions of each one of their components. All this data goes to a database of thousands, or even millions of entries, depending on how detailed the analysis is. There are more than 250,000 edible plant species in the world, according to the FAO, and uncountable varieties of each one of them.

然后要对食物进行分析。研究人员必须弄清楚每种植物性食材在分子水平上的组成成分,以及每种成分的比例。所有这些数据都将录入一个包含数千甚至数百万个条目的数据库中,条目数量取决于分析的详细程度。联合国粮农组织统计发现,世界上有25万多种可食用植物,而每种植物的变种不计其数。

As if the puzzle weren’t hard enough, there is also the issue of how these different components interact with each other. Get it wrong and certain combinations can produce unexpected and unpleasant tastes or undesired reactions. The problem, as San Martin points out, is that “the interactions between the compounds are very complex,” which means that many things can go wrong in unforeseeable ways.

如果这个问题还不够难,那么还有一个问题就是这些不同的成分是如何相互作用的。错误组合和特定组合会产生意想不到和不愉快的味道或不理想的反应。正如圣·马丁指出的那样,问题在于"化合物之间的相互作用非常复杂",这意味着许多事情会在无法预见的情况下出错。

Unravelling so many variables is a mind-boggling process. But this is exactly where AI can be useful. Instead of manually tasting and expecting to hit a jackpot by sheer chance, AI uses are more logical approach. It does so through machine learning, a technique that basically allows a computer to learn how to solve a problem by trying and failing at it many times. It is used for solving many different problems, from identifying your face in a picture to helping doctors spot cancer.

解析如此多的变量是一个烧脑的过程,但这正是人工智能一显身手的地方。人工智能用的是更合乎逻辑的方法,而不是完全靠碰运气的人工品尝。它是通过机器学习来实现的,这一技术使得计算机能够通过多次试错来解决问题。它被用于解决许多不同的问题,比如在图片中识别出人的面部,帮助医生发现癌症等。

While the AI doesn’t get it right at the first time, it improves with every mistake, often helped by human feedback.

虽然人工智能不会立刻得到正确的结果,但它在每次犯错时都能吸取教训并进行改善,这通常得益于人类提供的反馈。

The results can be surprising. Hampton Creek recently found the isolated protein of an Indian legume called mung bean has similar properties to scrambled eggs. One of NotCo’s most dazzling formulations is its chocolate prototype: a bizarre combination of broccoli, goji berries, champignon mushrooms and a nut, whose name, sadly, they won’t share with us.

结果可能令人感到惊讶。Hampton Creek公司最近发现,一种印度绿豆的分离蛋白具有类似炒鸡蛋的特性。NotCo公司最令人眼花缭乱的配方之一是它的巧克力原型产品,一个使用西兰花、枸杞、双孢蘑菇和坚果的奇特组合。可惜的是,他们不愿告诉我们是哪一种坚果。

So far, these companies have used AI-led approach to create emulsions: liquid foods like mayonnaise, scrambled egg replacements, or cookie dough. Solid foods are more complicated to mimic. These requires “the slow release of molecules, of crunchiness” that, as San Martin says, are part of the experience of biting and eating. It is like solving a 3D puzzle instead of just a 2D one. NotCo has a plan, though.

到目前为止,这些公司以人工智能为主导制造乳剂,即像蛋黄酱、炒蛋替代品、曲奇饼这样的流食。模仿生产固体食物更加复杂。正如圣·马丁所说,固体食物带有咬和咀嚼的体验,需要"缓慢释放分子和脆爽的口感"。这就像在解决三维难题而不是二维难题。不过NotCo公司已经有了相应计划。

“We are creating a milk that is just like cow milk,” says Zamora. “Not only with a similar, or better nutritional profile than cow milk, but also with its same functional structure.” By this he means it can be used in the same way cow’s milk currently is – for drinking, cooking or as a base for making cheese, yoghurt or ice cream. Except it would be a vegan product.

萨莫拉说:"我们正在制造一种牛奶,它就像真的牛奶一样,不仅与牛奶有相似或更好的营养成分,而且具有相同的功能结构。"萨莫拉的意思是,它可以像现在的牛奶那样被使用,比如用于饮用、烹饪或作为制作奶酪、酸奶或冰淇淋的原料。但它却是一种素食产品。

However, the big target is replacing meat, and both start-ups are applying different approaches to this muscular problem. Hampton Creek is cultivating muscle and fat cells in the laboratory, and is working on how to feed those cells with plant-based nutrients. NotCo is looking at ways to recreate meat with only plant-based ingredients.

然而,更大的目标是取代肉类,这两家初创公司都在用不同的方法解决这个跟棘手的问题。Hampton Creek公司正在实验室培育肌肉和脂肪细胞,并正在研究如何用植物性营养来喂养这些细胞。而NotCo公司正在寻找只用植物性成分再造肉类的方法。

This subtlety is illuminated by the name of NotCo’s AI robot: Giuseppe. It is named after Giuseppe Arcimboldo, a Renaissance painter who shaped human portraits with fruits and vegetables. “The animal ingredient is never an option for us,” says Muchnick.

两公司间的细微差别在Notco公司的人工智能机器人的名字上体现出来。Notco的人工智能机器人Giuseppe是以文艺复兴时期画家朱塞佩·阿尔钦博托(Giuseppe Arcimboldo)的名字命名的,后者擅长用水果和蔬菜塑造人像。穆奇尼克解释称:"对我们来说,绝不可能选择使用动物性原料。"

But even with AI, their progress is a painstakingly slow. The puzzle is almost impossibly delicate, and any small thing out of place can ruin it. It is a bit like building a house or a cathedral, explains San Martin. They are both made from the same basic building blocks, but one of them is far grander than the other. For those trying to change the way we eat, anything less than a cathedral will not do.

但即使有了人工智能,两家公司的进展还是非常缓慢。这个难题无以复加的复杂,任何一个细节错误都可能毁掉全局。圣·马丁解释说,这有点儿像盖房子或大教堂。它们都是用相同的石块建造的,但是有的石块非常大,有的又非常小。对努力改变人们饮食方式的人来说,这件事的难度不比建造一座大教堂容易。

Future challenges

未来的挑战

Creating these new foods, however, is only the first challenge. Convincing the world to eat them is another.

然而,创造这些新食物只是第一个挑战,说服人们吃它们则是另一回事。

“We change our diets extremely slowly,” says David Hughes, emeritus professor of food marketing at Imperial College London. Food consumption patterns are stubborn, even when there are good alternatives. Marketing is vital, but it is expensive. The $220m (£164m) Hampton Creek has received in investment, and the $2.6m (£1.9m) backing NotCo, are a far cry from the budgets wielded by the international food giants. Nestlé, the world’s biggest food company, is valued at $229.5bn (£170bn).

伦敦帝国理工学院(Imperial College London)食品营销学名誉教授大卫·休斯(David Hughes)表示:"我们的饮食习惯变化极其缓慢。"即使有更好的选择,食品消费模式也是根深蒂固的。营销推广至关重要,但是花费不菲。Hampton Creek公司获得的2.2亿美元投资,和NotCo公司260万美元的融资,甚至都远不及国际食品巨头的营销预算。世界上最大的食品公司雀巢(Nestlé)的市值高达2295亿美元。

Hughes believes there will be space for all these players in the future. The combination of health, environmental and animal welfare concerns “will drive more acceptance of these artificial intelligence products”. He believes they could become a significant, but still a minority, part of the protein market worldwide.

休斯认为,未来所有这些参与者都会有发展空间。健康、环境和动物福祉的结合"将促使人们更容易接受这些人工智能产品"。他相信,它们可能会成为全球蛋白质市场的重要组成部分,尽管可能仍然是少数人的选择。

But there is another problem that might prevent them getting the global reach they desire.

还有一个问题可能会阻碍它们获得渴望的全球影响力。

As it turns out, algorithms have their own quirks. “They are biased in terms of how do you feed them, how you interpret the data, and how you extract the data”, explains San Martin.

事实证明,算法也有自己的怪癖。圣·马丁解释说:"输入信息、解读数据以及提取数据的方式,可能会导致人工智能产生偏见。"

The problem is, food preferences rely heavily on cultural preferences: try getting Americans to like Marmite, for example. If these new foods are formulated to cater only for the tastes of the white Westerners operating the AI alchemists creating them, they may be doomed to fail. Tetrick insists he is trying to tackle this problem by hiring a team whose members come from many different parts of the world.

问题是,食物偏好严重依赖文化偏好。举例来说,很难让美国人喜欢上马麦酱。如果这些新食品只是迎合那些操作人工智能的西方白人的口味,那么它们可能注定会失败。蒂特里克坚持说,他正在试图通过雇佣来自世界各地的团队来解决这个问题。

While none of these companies have yet had issues with national health authorities over the safety of their products, this could too could create problems if they ever resort to any ingredient that hasn’t been used before as food.

虽然目前这些公司还没有因产品的安全性问题跟相关国家卫生部门发生纠纷,但如果他们使用以前未被用作食品的任何原料,就可能会造成问题。

But perhaps the knottiest problem of all will be something machines will probably never be able to solve. Whatever strange combinations these AIs find to replace meat, cheese, or eggs, they are likely to come up with a concoction that no one has eaten before. And humans are fickle creatures – old habits die hard.

但也许其中最棘手的问题将是一个机器可能永远无法解决的难题。无论人工智能找到了什么奇怪的组合来代替肉、奶酪或鸡蛋,它们很可能会创造出一种从未有人吃过的混合物。但是人类虽然是一群善变的生物,但是旧习难改。

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