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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.
與任何使用人工智能的識別軟件（如谷歌反向圖片搜索引擎）一樣，最大的障礙是處理不完整資料的片段，在這種情況下是在分子層面。
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·里德爾喜歡用一個更具體的例子來描述這項工作：“人工智能需要解決的挑戰是，如果我向你展示一張大象的圖片，電腦能否識別圖片中的內容？”
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.
假設你只有圖片的百分之一（可能是大象鼻子的幾個灰色像素），而其余99%是未知或難以理解的。
"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."
“我們現在可以制造一個仍然可以識別圖片中有一頭大象的人工智能嗎？”他問。“這就是我們開發的人工智能。最終，這就是它的作用。”
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.
另一個根本性的困境，特別是在兒科腦腫瘤的情況下，是醫院每年處理的病例可能少于一百個，這造成了數據稀疏問題。
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，你需要一個包含數千個病例的資料庫，甚至可以開始訓練像Sturgeon這樣的東西來執行腫瘤辨識。
(Compare that with ChatGPT, which trains itself on billions of freely available sentences on the internet.)
（與ChatGPT相比，ChatGPT利用互聯網上數十億個免費可用的句子進行自我訓練。）
How do you reconcile that small sample size with the need for unfathomably vast datasets?
如何協調較小的樣本量與難以想像的龐大數據集的需求？
For de Ridder and Tops, it meant getting creative.
對德·里德爾和托普斯來說，這意味著發揮創意。
The pair pulled data from existing tumor samples found in previously published studies. Even then, they were operating at a deficit.
兩人從先前發表的研究中發現的現有腫瘤樣本中提取了數據。即便如此，他們仍處于赤字狀態。
"Well, we had about 3,000 samples," de Ridder explains. "So not a whole lot."
“嗯，我們大約有3000個樣本，”德·里德爾解釋道。“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.
但從這3000個樣本中，他們能夠模擬數百萬個獨特的納米孔序列，用于訓練Sturgeon--類似于《黑客帝國》中的尼奧如何將數百年的功夫訓練上傳到他的大腦中。
"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."
“我們總共執行了4500萬次這樣的操作，以獲得具有訓練非常復雜網絡所需容量的資料集，”德·里德爾說。“你瞧，這似乎有效。”
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.
雖然Sturgeon已被用于幫助即時決策的研究，但瑪西瑪公主小兒腫瘤中心團隊正在設計臨床試驗，以更好地了解Sturgeon的影響。
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.
理論上，分子定序可以擴大范圍，幫助辨識腦腫瘤以外的疾病和疾病：黑色素瘤、肺部真菌感染、骨髓纖維化等罕見血液疾病。
Using DNA to instantly recognize rare or difficult-to-diagnose ailments could radically reshape the landscape of medicine.
使用DNA立即識別罕見或難以診斷的疾病可能會從根本上重塑醫學格局。
Within the field of neurosurgery, some scientists are already theorizing that AI could be paired with surgical robots to automate complex procedures.
在神經外科領域，一些科學家已經推測人工智能可以與手術機器人配合使用，以實現復雜手術的自動化。
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.
同時，哈佛大學和谷歌的研究人員最近制作了第一張一立方毫米腦組織的3D地圖，這可能提供更多方法來理解我們為什么思考我們該如何做、何時可能出現認知問題，甚至我們如何體驗情緒。
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.
但進步是迭代的。設計上很慢。醫療監管機構仍需要確保Sturgeon及其類似技術的安全性，這可能需要五年或更長時間。
"We have to prove it," says Hoving. "We have to give it a background that is really trustworthy."
“我們必須證明這一點，”霍文說。“我們必須給它一個真正值得信賴的背景。”
Though initially an AI neophyte, Hoving has become an evangelist for the possibilities AI can offer, particularly from an augmentative standpoint.
盡管霍文最初是一名人工智能新手，但他已經成為人工智能所能提供的可能性的傳播者，特別是從增強的角度來看。
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年內，神經外科醫生可以佩戴一副人工智能眼鏡，能夠即時找出并識別癌癥：用于腫瘤搜尋的終結者視覺。
"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.
“我認為有很多技術，特別是在成像和混合現實類型的技術方面，將對我們有所幫助，”霍文說。
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.
最終將由神經外科醫生做出最終決定，就像他們一直所做的那樣，但他們將能夠以更少的猜測來做到這一點。
