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人工智能生产的烤牛肉风味

通过与微软的合作,芬美意香料公司能够利用其整个原材料数据库,找到各种成分的完美结合,从而产生“轻度烤牛肉”的味道。该产品代表了该行业的一个里程碑,因为他们声称这是有史以来第一款由人工智能(AI)创造的口味。

In a move toward enhancing the eating experience of plant-based ‘meat’ products, a Swiss perfume and taste company has announced its creation of the first AI-produced flavour.

为了提高植物性“肉类”产品的食用体验,一家瑞士香水和口味公司宣布,它创造了第一款人工智能制作的香精。

人工智能生产的烤牛肉风味

In collaboration with Microsoft, the flavour company Firmenich was able to draw on the entirety of its raw material database to find the perfect marriage of components to produce a ‘lightly grilled beef’ taste. The product represents a milestone in the industry, as they claim it is the first ever flavour created by Artificial Intelligence (AI).

通过与微软的合作,芬美意香料公司能够利用其整个原材料数据库,找到各种成分的完美结合,从而产生“轻度烤牛肉”的味道。该产品代表了该行业的一个里程碑,因为他们声称这是有史以来第一款由人工智能(AI)创造的口味。

Commenting on the company’s use of the right combination of technology and skill, Eric Saracchi, Firmenich’s Chief Digital and Information Officer, said: “Firmenich is marrying the most fundamental elements of our DNA: ground-breaking innovation with the unique creativity of our Flavourits for enhanced formulation.

芬美意首席数字和信息官Eric Saracchi在评论公司对技术和技能的正确结合时表示:“芬美意将我们DNA中最基本的元素结合在一起:突破性的创新与我们调香师的独特创造力,以增强配方。

AI enables us to explore new boundaries by empowering our Creators with a precise formula starting point, as well as additional suggestions for optimised ingredient combinations from which they can create bespoke tastes,” he said.

人工智能使我们能够探索新的界限,赋予我们的创造者一个精确的配方起点,以及优化成分组合的额外建议,他们可以从中创造定制的口味,”他说。

Responding to a changing landscape with AI

用人工智能应对不断变化的环境

The Covid pandemic has sharpened the focus of much in our lives, and this includes the food innovation landscape, according to Firmenich Flavors President, Emmanuel Butstraen. “We must understand and respond to these rapidly-evolving needs with more creativity at an even faster pace,” he explained. “Whether through offering moments of comfort and delight or addressing the larger shift towards healthier food and beverages. The exciting addition of AI allows us to better leverage different raw materials and explore new creative leads.”

芬美意香精总裁Emmanuel Butstraen表示,新冠疫情使我们生活中的许多方面都变得更加关注,其中包括食品创新领域。他解释说:“我们必须以更快的速度,以更大的创造力,了解和回应这些快速变化的需求。”“无论是通过提供舒适和快乐的时刻,还是通过向更健康的食品和饮料转变。令人兴奋的是,人工智能的加入使我们能够更好地利用不同的原材料,探索新的创意线索。”

He explained how AI has accelerated their time to market and facilitated the inclusion of specific product parameters, such as 100 percent natural ingredients and the latest regulatory requirements.

他解释了人工智能如何加快了他们的上市时间,并促进了特定产品参数的加入,比如100%天然成分和最新的监管要求。

The creation of this AI-aided grilled beef flavour illustrates an exciting new avenue in the realm of food product development. Good news for food companies and consumers alike.

这种人工智能辅助烤牛肉风味的创造说明了食品开发领域令人兴奋的新途径。这对食品公司和消费者来说都是好消息。

How AI Will Help Us Find New, Innovative Flavors of the Future

人工智能将如何帮助我们找到新的、创新的未来口味

As AI is increasingly being used in fields like healthcare, research and finance, it may very well be possible for AI to lead the way toward novel flavor experiences, as a recent partnership between tech multinational IBM and American spice, marinade and condiment company McCormick & Company is suggesting.

随着人工智能越来越多地应用于医疗保健、研究和金融等领域,人工智能很有可能引领创新口味体验,正如科技跨国公司IBM与美国香料、腌料和调味品公司麦考密克公司(McCormick & company)最近的合作所表明的那样。

人工智能生产的烤牛肉风味

Wasabi-flavored caviar crepes, bruschetta tandoori chicken, charcoal ice cream — these all sound like the gastronomic inventions of a highly creative human chef, intent upon taking our tastebuds to unexpected dimensions. But what if artificially intelligent machines could also dream up new, innovative food recipes, as well as improve upon already existing cuisines and food product formulations?

芥末味的鱼子酱薄饼、烤面包烤鸡、木炭冰淇淋——这些听起来都像是一位极具创造力的人类厨师的美食发明,意图将我们的味蕾带到意想不到的境界。但是,如果人工智能机器也能想出新的、创新的食物配方,并改进现有的菜肴和食品配方,那会怎么样呢?

As AI is increasingly being used in fields like healthcare, research and finance, it may very well be possible for AI to lead the way toward novel flavor experiences, as a recent partnership between tech multinational IBM and American spice, marinade and condiment company McCormick & Company is suggesting.

随着人工智能越来越多地应用于医疗保健、研究和金融等领域,人工智能很有可能引领创新口味体验,正如科技跨国公司IBM与美国香料、腌料和调味品公司麦考密克公司(McCormick & company)最近的合作所表明的那样。

Best known for its AI system Watson, as well as for its recent forays into AI-assisted authentication, interactive design and cognitive computing, IBM’s four-year-long collaboration with McCormick aims to ease the process of enhancing and developing new food products by using algorithms that can quickly synthesize available data on the vast number of possible ingredient combinations — culled over decades of research and taste-testing — in order to generate new flavors and products.

IBM与麦考密克公司为期四年的合作以其人工智能系统沃森(Watson)以及最近涉足人工智能辅助认证、交互设计和认知计算而闻名,旨在通过使用算法来快速合成大量可能的成分组合的可用数据(经过数十年的研究和口味测试),从而简化改进和开发新食品的过程,从而产生新的口味和产品。

AI Flavor Assistant

AI调味助手

As IBM notes in a recent blog post, the conventional process of devising a new food product can be quite time-consuming, sometimes taking months or even years to evolve from the conceptual stage to being finally ready for the market. For starters, the science behind how humans experience the vast spectrum of flavors is complex and not completely understood. Tasting the flavor of a delicious morsel of food doesn’t just occur on the tongue; it also involves other senses like smell and visually appreciating its appearance. Alternatively, taste perception can be genetically influenced and isn’t necessarily restricted to the standard sweet, sour, salty and bitter tastes — for example, we can also detect other possibilities like the savory flavors of meat and potatoes.

正如IBM在最近的一篇博客文章中指出的那样,设计一种新食品的传统过程可能相当耗时,有时需要数月甚至数年的时间才能从概念阶段发展到最终为市场做好准备。首先,人类如何体验各种各样的味道背后的科学是复杂的,还没有完全被理解。品尝一口美味食物的味道不仅仅发生在舌头上;它还涉及其他感官,如嗅觉和视觉上欣赏它的外观。另外,味觉可以受到基因的影响,并不一定局限于标准的甜、酸、咸和苦的味道——例如,我们也可以检测到其他的可能性,比如肉和土豆的咸味。

So it’s evident that the process of developing new flavors involves a lot of complex variables, and that’s where IBM’s system can help simplify the process. It does so by detecting patterns gleaned from existing flavor formulas, the inventory of raw ingredients, results from consumer taste tests, and even sales data, and learning from these patterns in order to generate new flavor combinations.

因此,很明显,开发新口味的过程涉及许多复杂的变量,而IBM的系统可以帮助简化这一过程。它通过检测从现有风味配方、原材料库存、消费者口味测试结果甚至销售数据中收集到的模式,并从这些模式中学习,以产生新的风味组合。

In contrast, when confronted with the massive number of possible ingredients and combinations, a human product developer might inadvertently find themselves relying on tried-and-true components and flavor profiles in order to get the job done quickly. These experts often work in teams, drawing upon the collective knowledge of chefs, chemists and other food professionals, refining a concept iteratively many dozens of times until it’s ready for the market.

相比之下,当面对大量可能的成分和组合时,人类产品开发人员可能会无意中发现自己依赖于可靠的成分和风味特征,以便快速完成工作。这些专家通常在团队中工作,利用厨师、化学家和其他食品专业人士的集体知识,反复完善一个概念,直到它准备好进入市场。

Moreover, cost targets and even the cultural context of where the product is intended for sale are critical factors too. Thus, having an intelligent assistant that merges this collective expertise will not only streamline the process, but also help human experts find better, more creative and cost-effective solutions quickly, without having to depend on certain mainstays just because they worked in the past.

此外,成本目标,甚至是产品销售的文化背景也是关键因素。因此,拥有一个整合这些集体专业知识的智能助手不仅可以简化流程,还可以帮助人类专家快速找到更好、更有创意和更具成本效益的解决方案,而不必仅仅因为他们过去工作过而依赖于某些主流。

人工智能生产的烤牛肉风味

AI-Generated Novelty

AI主导的创新

In addition, the AI system could also propose ingredient substitutions, as well as different component ratios in regard to how often they are used in existing products. It can even measure the “novelty” of a new flavor, based on what IBM calls a “derived distance function,” which is described as “the larger the distance between a flavor formula and its nearest neighbors, the more novel the flavor formula is predicted to be.”

此外,人工智能系统还可以提出替代成分,以及根据它们在现有产品中的使用频率提出不同的成分比例。它甚至可以根据IBM所谓的“派生距离函数”来衡量一种新口味的“新颖性”,该函数被描述为“一种风味配方与其最近的邻居之间的距离越大,预计这种风味配方就越新颖。”

“A key part to building an AI system that is an essential tool in the daily workflow of product developers is supporting different degrees of novelty,” explained IBM. “One size doesn’t fit all. In some scenarios, the goal is to optimize a flavor formula by tweaking it to perfection. For instance, there are a multitude of flavor nuances from different vanilla beans sourced from many parts of the world. What is the best combination of vanillas to deliver the desired flavor experience? In other scenarios, the goal is to develop truly novel flavor experience. Here, the variation or distance between ingredients (e.g. vanilla vs strawberry) is likely more significant than the choice within any one ingredient family (e.g. vanilla). Our system learns and uses a distance model to suggest desired flavor formulas.”

IBM解释说:“要构建一个在产品开发人员的日常工作流程中必不可少的人工智能系统,一个关键部分是支持不同程度的新颖性。”“一个尺码不适合所有人。在某些情况下,目标是通过将其调整到完美来优化风味配方。例如,来自世界许多地方的不同香草豆有许多细微的味道差别。香草的最佳组合是什么?在其他情况下,目标是开发真正新颖的风味体验。在这里,成分之间的差异或距离(例如香草和草莓)可能比任何一种成分家族(例如香草)的选择更重要。我们的系统学习并使用距离模型来推荐理想的风味配方。”

Not surprisingly, such a data-driven, pattern-detecting, novelty-generating system could be “generalized” and translated to other fields, such as in the development of new perfume scents, cosmetics, household and industrial products, and even building materials (hopefully of the durable and ecologically friendly kind).

毫不奇怪,这样一个数据驱动、模式检测、新奇产生的系统可以被“推广”并转化到其他领域,比如开发新的香水气味、化妆品、家居和工业产品,甚至建筑材料(希望是耐用和生态友好的那种)。

In the meantime, if all goes well, the two companies plan to gradually deploy this new AI-assisted platform in all of McCormick’s laboratories around the world by 2021. For now, we can expect to at least see AI-improved versions of seasoning mixes for Tuscan chicken, bourbon pork tenderloins, and New Orleans-style sausage on the shelves later this year — and likely more mind-blowingly innovative, AI-generated flavors in the near future.

与此同时,如果一切顺利,两家公司计划到2021年在麦考密克全球所有实验室逐步部署这种新的人工智能辅助平台。就目前而言,我们至少可以在今年晚些时候看到人工智能改进的托斯卡纳鸡肉、波旁猪里脊和新奥尔良风味香肠的调味料混合物,在不久的将来,人工智能生成的调味料可能会更加创新。

 

 

 

原创文章,作者:ifyoung,如若转载,请注明出处:https://www.drugfoodai.com/ai-meat-flavour.html

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