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The big obstacle, as with any identification software that uses AI, like Google Reverse Image Search, is that one is dealing with fragments of incomplete data, in this case on a molecular level.
与任何使用人工智能的识别软件（如谷歌反向图片搜索引擎）一样，最大的障碍是处理不完整资料的片段，在这种情况下是在分子层面。
De Ridder likes to describe the work with a more specific example: "The challenge that the AI needs to solve is, if I show you a picture of an elephant, does a computer recognize what's in the picture?"
德·里德尔喜欢用一个更具体的例子来描述这项工作：“人工智能需要解决的挑战是，如果我向你展示一张大象的图片，电脑能否识别图片中的内容？”
Let's say you only have one percent of the picture -- maybe a few gray pixels of the elephant's trunk -- and the other 99 percent is unknown or inscrutable.
假设你只有图片的百分之一（可能是大象鼻子的几个灰色像素），而其余99%是未知或难以理解的。
"Can we now make an AI that can still recognize that there's an elephant in the picture?" he asks. "And that's the AI that we developed. Ultimately, that's what it does."
“我们现在可以制造一个仍然可以识别图片中有一头大象的人工智能吗？”他问。“这就是我们开发的人工智能。最终，这就是它的作用。”
The other fundamental quandary, especially in the case of pediatric brain tumors, is that hospitals may handle fewer than a hundred cases a year, which creates a data sparsity problem.
另一个根本性的困境，特别是在儿科脑肿瘤的情况下，是医院每年处理的病例可能少于一百个，这造成了数据稀疏问题。
With AI, you need a database in the thousands of cases to even begin training something like Sturgeon to perform tumor identification.
有了人工智能，你需要一个包含数千个病例的资料库，甚至可以开始训练像Sturgeon这样的东西来执行肿瘤辨识。
(Compare that with ChatGPT, which trains itself on billions of freely available sentences on the internet.)
（与ChatGPT相比，ChatGPT利用互联网上数十亿个免费可用的句子进行自我训练。）
How do you reconcile that small sample size with the need for unfathomably vast datasets?
如何协调较小的样本量与难以想像的庞大数据集的需求？
For de Ridder and Tops, it meant getting creative.
对德·里德尔和托普斯来说，这意味著发挥创意。
The pair pulled data from existing tumor samples found in previously published studies. Even then, they were operating at a deficit.
两人从先前发表的研究中发现的现有肿瘤样本中提取了数据。即便如此，他们仍处于赤字状态。
"Well, we had about 3,000 samples," de Ridder explains. "So not a whole lot."
“嗯，我们大约有3000个样本，”德·里德尔解释道。“所以不是很多。”
But from those 3,000 samples, they were able to fabricate simulations for millions of unique nanopore sequences that they used to train Sturgeon -- similar to how Neo in The Matrix gets centuries of kung fu training uploaded to his brain.
但从这3000个样本中，他们能够模拟数百万个独特的纳米孔序列，用于训练Sturgeon--类似于《黑客帝国》中的尼奥如何将数百年的功夫训练上传到他的大脑中。
"We did this 45 million times total to get to a dataset that has the volume required to train very complex networks," says de Ridder. "And lo and behold, that appeared to work."
“我们总共执行了4500万次这样的操作，以获得具有训练非常复杂网络所需容量的资料集，”德·里德尔说。“你瞧，这似乎有效。”
While Sturgeon is already being used in a research capacity to help with real-time decision-making, the Princess Máxima team is designing clinical trials to better understand Sturgeon's impact.
虽然Sturgeon已被用于帮助即时决策的研究，但玛西玛公主小儿肿瘤中心团队正在设计临床试验，以更好地了解Sturgeon的影响。
In theory, molecular sequencing could be broadened to help identify diseases and conditions beyond brain tumors: melanomas, fungal infections in the lungs, rare blood disorders like myelofibrosis.
理论上，分子定序可以扩大范围，帮助辨识脑肿瘤以外的疾病和疾病：黑色素瘤、肺部真菌感染、骨髓纤维化等罕见血液疾病。
Using DNA to instantly recognize rare or difficult-to-diagnose ailments could radically reshape the landscape of medicine.
使用DNA立即识别罕见或难以诊断的疾病可能会从根本上重塑医学格局。
Within the field of neurosurgery, some scientists are already theorizing that AI could be paired with surgical robots to automate complex procedures.
在神经外科领域，一些科学家已经推测人工智能可以与手术机器人配合使用，以实现复杂手术的自动化。
Meanwhile, researchers at Harvard and Google recently produced the first 3D map of one cubic millimeter of brain tissue, which may offer even more ways to understand why we think how we do, when something may be cognitively amiss, or even how we experience emotion.
同时，哈佛大学和谷歌的研究人员最近制作了第一张一立方毫米脑组织的3D地图，这可能提供更多方法来理解我们为什么思考我们该如何做、何时可能出现认知问题，甚至我们如何体验情绪。
But progress is iterative. Slow by design. Medical regulators still need to be satisfied that Sturgeon, and technology like it, is safe, which could take five years or more.
但进步是迭代的。设计上很慢。医疗监管机构仍需要确保Sturgeon及其类似技术的安全性，这可能需要五年或更长时间。
"We have to prove it," says Hoving. "We have to give it a background that is really trustworthy."
“我们必须证明这一点，”霍文说。“我们必须给它一个真正值得信赖的背景。”
Though initially an AI neophyte, Hoving has become an evangelist for the possibilities AI can offer, particularly from an augmentative standpoint.
尽管霍文最初是一名人工智能新手，但他已经成为人工智能所能提供的可能性的传播者，特别是从增强的角度来看。
Imagine, in 10 to 15 years, a neurosurgeon could wear a pair of AI-enabled glasses that would be able to pinpoint and identify cancers in real time: Terminator vision for tumor hunting.
想像一下，在10到15年内，神经外科医生可以佩戴一副人工智能眼镜，能够即时找出并识别癌症：用于肿瘤搜寻的终结者视觉。
"I think there's a lot of technology, especially in imaging and in this mixed-reality type of thing, that will help us," says Hoving.
“我认为有很多技术，特别是在成像和混合现实类型的技术方面，将对我们有所帮助，”霍文说。
It will ultimately be up to neurosurgeons to make the final determination, as they always have -- but they'll be able to do so with far less guesswork.
最终将由神经外科医生做出最终决定，就像他们一直所做的那样，但他们将能够以更少的猜测来做到这一点。
