Computer algorithms today are performing incredible tasks with high accuracies, at a massive scale, using human-like intelligence. And this intelligence of computers is often referred to as AI or artificial intelligence. AI is poised to make an incredible impact on our lives in the future. Today, however, we still face massive challenges in detecting and diagnosing several life-threatening illnesses, such as infectious diseases and cancer. Thousands of patients every year lose their lives due to liver and oral cancer.
現今的電腦演算法能夠執行 很了不起的工作任務, 有高度的精確性,規模可以很大, 且用的是類似人類的智慧。 這種電腦的智慧通常被稱為 AI, 也就是人工智慧。 人工智慧已經準備好要對 我們未來的生活造成衝擊。 然而我們現今仍然面臨很大的挑戰, 包括偵測與診斷數種 會威脅生命的疾病, 比如感染性疾病以及癌症。 每年,有數千名病人 因為肝癌或口腔癌而喪命。
Our best way to help these patients is to perform early detection and diagnoses of these diseases. So how do we detect these diseases today, and can artificial intelligence help? In patients who, unfortunately, are suspected of these diseases, an expert physician first orders very expensive medical imaging technologies such as fluorescent imaging, CTs, MRIs, to be performed. Once those images are collected, another expert physician then diagnoses those images and talks to the patient. As you can see, this is a very resource-intensive process, requiring both expert physicians, expensive medical imaging technologies, and is not considered practical for the developing world. And in fact, in many industrialized nations, as well.
若要幫助這些病人的最好方法 就是早期偵測並診斷出這些疾病。 現今我們要如何偵測出這些疾病? 人工智慧能幫得上忙嗎? 對於很不幸被懷疑可能 得了這些疾病的病人, 專業的醫生首先會囑咐 採用非常昂貴的醫療成像技術, 例如螢光成像、 電腦斷層掃瞄、核磁共振。 一旦收集到了這些影像, 會有另一位專業醫生根據 這些影像做診斷,並和病人談。 不難看出,這是非常耗資源的過程, 需要專業的醫生 和昂貴的醫療成像技術兩者, 而這在開發中國家是不實際的; 事實上,在許多工業化的國家亦然。
So, can we solve this problem using artificial intelligence? Today, if I were to use traditional artificial intelligence architectures to solve this problem, I would require 10,000 -- I repeat, on an order of 10,000 of these very expensive medical images first to be generated. After that, I would then go to an expert physician, who would then analyze those images for me. And using those two pieces of information, I can train a standard deep neural network or a deep learning network to provide patient's diagnosis. Similar to the first approach, traditional artificial intelligence approaches suffer from the same problem. Large amounts of data, expert physicians and expert medical imaging technologies.
所以,我們能用人工智慧 來解決這個問題嗎? 現今,若我要用傳統人工智慧架構 來解決這個問題, 我會需要一萬—— 我重覆一次,大約一萬張 這種非常昂貴的醫療影像 先被產生出來。 產生出來後,接著去找專業醫生, 來為我分析這些影像。 用這兩種資訊, 我就能訓練標準的 深度類神經網路或深度學習網路 來提供對病人的診斷。 和第一個方法很類似, 傳統人工智慧方法 也會遇到同樣的問題。 大量的資料、專業醫生, 以及專業醫療成像技術。
So, can we invent more scalable, effective and more valuable artificial intelligence architectures to solve these very important problems facing us today? And this is exactly what my group at MIT Media Lab does. We have invented a variety of unorthodox AI architectures to solve some of the most important challenges facing us today in medical imaging and clinical trials.
我們是否能發明 更有擴展性、更有效, 且更有價值的人工智慧架構, 來解決我們現今所面臨的 這些非常重要的問題? 這就是我的團隊在麻省理工學院 媒體實驗室在做的事。 我們已經發明了多種 非正統的人工智慧架構 來解決我們現今在醫療成像 及臨床實驗方面 所面臨的一些最重要的挑戰。
In the example I shared with you today, we had two goals. Our first goal was to reduce the number of images required to train artificial intelligence algorithms. Our second goal -- we were more ambitious, we wanted to reduce the use of expensive medical imaging technologies to screen patients. So how did we do it?
在今天我和各位分享的 例子中,我們有兩個目標。 我們的第一個目標是要減少 訓練人工智慧演算法 所需要的影像數量。 我們的第二個目標—— 我們的野心更大, 我們想要減少使用昂貴醫療成像技術 來篩選病人。 我們要怎麼做?
For our first goal, instead of starting with tens and thousands of these very expensive medical images, like traditional AI, we started with a single medical image. From this image, my team and I figured out a very clever way to extract billions of information packets. These information packets included colors, pixels, geometry and rendering of the disease on the medical image. In a sense, we converted one image into billions of training data points, massively reducing the amount of data needed for training.
針對第一個目標, 不像傳統人工智慧一開始 要用到數萬張非常 昂貴的醫療影像, 我們反而從單一張醫療影像開始。 從這張影像,我和我的團隊 想出了一個非常聰明的方法 來取出數十億個資訊封包。 這些資訊封包包括用 顏色、像素、幾何學, 在醫療影像上呈現疾病。 在某種意義上,我們是把一張影像 轉變為數十億個訓練資料點, 大大減少了訓練所需要的資料量。
For our second goal, to reduce the use of expensive medical imaging technologies to screen patients, we started with a standard, white light photograph, acquired either from a DSLR camera or a mobile phone, for the patient. Then remember those billions of information packets? We overlaid those from the medical image onto this image, creating something that we call a composite image. Much to our surprise, we only required 50 -- I repeat, only 50 -- of these composite images to train our algorithms to high efficiencies.
至於第二個目標, 也就是減少使用昂貴的 醫療成像技術來篩選病人, 我們一開始使用的是 一張病人的標準白光照片, 可以用數位單眼相機或手機來拍攝。 接著,還記得 那數十億個資訊封包嗎? 我們將那些來自醫療影像的 封包疊到這張影像上, 創造出我們所謂的合成影像。 很讓我們驚訝的是, 我們只需要五十張—— 我重覆一次,只要五十張—— 這種合成影像,就能把我們的 演算法訓練到很高效能的程度。
To summarize our approach, instead of using 10,000 very expensive medical images, we can now train the AI algorithms in an unorthodox way, using only 50 of these high-resolution, but standard photographs, acquired from DSLR cameras and mobile phones, and provide diagnosis. More importantly, our algorithms can accept, in the future and even right now, some very simple, white light photographs from the patient, instead of expensive medical imaging technologies.
總結一下我們的方法, 我們不需要使用一萬張 非常昂貴的醫療影像, 我們現在可以用非正統的方法 來訓練人工智慧演算法, 只要用五十張高解析度的 一般標準照片, 用數位單眼相機或手機來拍攝即可, 這樣就能提供出診斷結果。 更重要的是, 在未來,甚至在現在, 我們的演算法能接受 病人非常簡單的白光照片, 取代昂貴的醫療成像技術。
I believe that we are poised to enter an era where artificial intelligence is going to make an incredible impact on our future. And I think that thinking about traditional AI, which is data-rich but application-poor, we should also continue thinking about unorthodox artificial intelligence architectures, which can accept small amounts of data and solve some of the most important problems facing us today, especially in health care.
我相信我們已經準備好 要進入一個新時代, 在這個時代,人工智慧 將會對我們的未來有很大的衝擊。 想想傳統人工智慧, 它在資料上很豐富, 但在應用上很有限, 我們應該要持續思考 有沒有其他非正統的 人工智慧架構, 能夠接受更少量的資料, 並解決一些現今我們 面臨最重要的問題, 特別是健康照護問題。
Thank you very much.
非常謝謝。
(Applause)
(掌聲)