演講MP3+雙語文稿:人工智能發(fā)展?jié)摿μ^可怕，未來是福是禍?
聽力課堂TED音頻欄目主要包括TED演講的音頻MP3及中英雙語文稿，供各位英語愛好者學習使用。本文主要內容為演講MP3+雙語文稿:人工智能發(fā)展?jié)摿μ^可怕，未來是福是禍?，希望你會喜歡！
【演講人】Leila Pirhaji
【演講主題】《人工智能和代謝產物的潛力》
【演講文稿-中英文】
翻譯者 Carol Wang 校對 Jiasi Hao
In 2003, when we sequenced the human genome, we thought we would have the answer to treat many diseases. But the reality is far from that, because in addition to our genes, our environment and lifestyle could have a significant role in developing many major diseases.
在 2003 年， 當我們測序人類基因組時， 我們以為會找到 治療多種疾病的答案。 但是實際情況遠非如此， 因為除了我們的基因， 我們的生存環(huán)境和生活方式 也可能導致多種重大疾病。
One example is fatty liver disease, which is affecting over 20 percent of the population globally, and it has no treatment and leads to liver cancer or liver failure. So sequencing DNA alone doesn't give us enough information to find effective therapeutics.
例如，影響全球 超過 20％ 人口的脂肪肝， 沒有任何有效的治療方法， 而且最終可發(fā)展為肝癌 或肝衰竭。 因此，單純的 DNA 測序 無法提供足夠信息， 幫助我們尋找有效治療方法。
On the bright side, there are many other molecules in our body. In fact, there are over 100,000 metabolites. Metabolites are any molecule that is supersmall in their size. Known examples are glucose, fructose, fats, cholesterol -- things we hear all the time. Metabolites are involved in our metabolism. They are also downstream of DNA, so they carry information from both our genes as well as lifestyle. Understanding metabolites is essential to find treatments for many diseases.
好消息是，我們體內 還有許多其他分子。 實際上，有超過 10 萬多種代謝物。 代謝物是體積很小的分子， 像我們常聽說的： 葡萄糖、果糖、脂肪、膽固醇等。 代謝物參與我們的新陳代謝， 它們在 DNA 的下游， 因此，它們攜帶著來自基因 和我們生活方式的信息。 了解代謝物對尋找許多疾病的 治療方法至關重要。
I've always wanted to treat patients. Despite that, 15 years ago, I left medical school, as I missed mathematics. Soon after, I found the coolest thing: I can use mathematics to study medicine. Since then, I've been developing algorithms to analyze biological data. So, it sounded easy: let's collect data from all the metabolites in our body, develop mathematical models to describe how they are changed in a disease and intervene in those changes to treat them.
我一直想治病救人， 但盡管如此，在十五年前， 我因為喜歡數學 而離開了醫(yī)學院。 不久之后，我發(fā)現了最酷的東西： 我可以使用數學來研究醫(yī)學。 從那時起，我一直在開發(fā) 用于分析生物學數據的算法。 這聽起來很簡單： 我們先收集體內所有代謝物， 然后，開發(fā)數學模型 描述疾病中的代謝物變化， 并通過干預這些變化來進行治療。
Then I realized why no one has done this before: it's extremely difficult.
然后，我終于明白 以前為何沒人做這件事了： 這真是太困難了。
（Laughter）
（笑聲）
There are many metabolites in our body. Each one is different from the other one. For some metabolites, we can measure their molecular mass using mass spectrometry instruments. But because there could be, like, 10 molecules with the exact same mass, we don't know exactly what they are, and if you want to clearly identify all of them, you have to do more experiments, which could take decades and of dollars.
我們體內有許多代謝產物， 種類繁多。 對于某些代謝物，我們可以使用 質譜儀來檢測其分子量。 而質量完全相同的分子 可能有 10 種之多， 我們分不清誰是誰， 如果想識別所有這些分子， 則必須進行更多實驗， 這可能需要幾十年、 數十億美元。
So we developed an artificial intelligence, or AI, platform, to do that. We leveraged the growth of biological data and built a database of any existing information about metabolites and their interactions with other molecules. We combined all this data as a meganetwork. Then, from tissues or blood of patients, we measure masses of metabolites and find the masses that are changed in a disease. But, as I mentioned earlier, we don't know exactly what they are. A molecular mass of 180 could be either the glucose, galactose or fructose. They all have the exact same mass but different functions in our body. Our AI algorithm considered all these ambiguities. It then mined that meganetwork to find how those metabolic masses are connected to each other that result in disease. And because of the way they are connected, then we are able to infer what each metabolite mass is, like that 180 could be glucose here, and, more importantly, to discover how changes in glucose and other metabolites lead to a disease. This novel understanding of disease mechanisms then enable us to discover effective therapeutics to target that.
為了做這件事，我們開發(fā)了 人工智能（AI）平臺， 我們利用生物數據的增長， 建立了一個數據庫， 包含代謝物現有信息 及與其它分子的相互作用的數據。 我們將所有這些數據 組合成了一個大型網絡， 然后，在患者的組織或血液中， 測量代謝物的質量， 并尋找因疾病 而產生變化的代謝物的質量。 但是，正如我之前提到的， 我們并不知道是什么代謝物。 分子量為 180 的代謝物 可以是葡萄糖、半乳糖或果糖， 在我們體內，它們的質量完全相同， 但功能不同。 我們的 AI 算法 考慮了所有這些可能。 然后，會挖掘那個巨型網絡的數據， 以發(fā)現那些代謝物 如何相互關聯(lián)而導致疾病。 根據它們的關聯(lián)方式， 我們就能推斷出 每個代謝物的質量， 如 180 分子量的 可能是葡萄糖， 更重要的是， 發(fā)現葡萄糖和其他代謝物的變化 如何導致疾病。 對疾病機制的這種新穎理解， 使我們能夠發(fā)現 針對該疾病的有效療法。
So we formed a start-up company to bring this technology to the market and impact people's lives. Now my team and I at ReviveMed are working to discover therapeutics for major diseases that metabolites are key drivers for, like fatty liver disease, because it is caused by accumulation of fats, which are types of metabolites in the liver. As I mentioned earlier, it's a huge epidemic with no treatment.
憑借該技術，我們成立了 一家初創(chuàng)公司， 將該技術推向市場， 進而影響人們的生活。 現在，我們的 ReviveMed 團隊 正努力尋找主要代謝疾病的療法， 例如脂肪肝， 因為它由脂肪堆積造成， 而脂肪是肝臟中的代謝物。 如前所述，這種大型流行病 尚無有效療法。
And fatty liver disease is just one example. Moving forward, we are going to tackle hundreds of other diseases with no treatment. And by collecting more and more data about metabolites and understanding how changes in metabolites leads to developing diseases, our algorithms will get smarter and smarter to discover the right therapeutics for the right patients. And we will get closer to reach our vision of saving lives with every line of code.
脂肪肝只是其中一個例子， 展望未來，我們將研究其它幾百種 尚無有效療法的疾病。 通過收集更多代謝物的數據， 了解代謝物的變化 如何導致疾病發(fā)展， 我們的算法會逐步完善， 為某些患者找到合適的療法。 而且，我們將更加接近我們的愿景： 用程序代碼拯救生命。
Thank you.
謝謝。
（Applause）
（掌聲）
