「釋義」

機器學習是人工智能的一個分支。

人工智能的研究歷史有著一條從以“推理”為重點，到以“知識”為重點，再到以“學習”為重點的自然、清晰的脈絡。

機器學習是實現(xiàn)人工智能的一個途徑，即以機器學習為手段解決人工智能中的問題。機器學習在近30多年已發(fā)展為一門多領域交叉學科。

「應用場景」

過去十年人類在人工智能，即機器學習的各個方面都取得了卓越進展。亞馬遜、蘋果、Facebook和谷歌等科技巨頭利用這種通過數(shù)據(jù)輸入進行預測的技術，極大改進了自身產(chǎn)品。很多初創(chuàng)企業(yè)也借此推出新產(chǎn)品和平臺，有時甚至可以和大型技術公司相抗衡。

The past decade has brought?tremendous advances?in an exciting dimension of artificial intelligence—machine learning. This technique for taking data inputs and turning them into predictions has enabled tech giants such as Amazon, apple, Facebook, and Google to dramatically improve their products. It has also spurred start-ups to launch new products and platforms, sometimes even in competition with Big Tech.

位于多倫多的初創(chuàng)企業(yè)BenchSci就是如此，該公司致力于縮短藥物研發(fā)流程。醫(yī)藥公司內(nèi)部數(shù)據(jù)庫和公開發(fā)表的科研論文數(shù)量龐大，科學家搜索時無異于大海撈針，該公司的目標是降低信息篩選難度，將精力集中在其中最關鍵的信息上。為篩選可供臨床實驗的新藥，科學家需要進行大量耗時耗錢的實驗。BenchSci公司注意到如果科學家能從已經(jīng)進行的大量實驗中提取更精準的信息，可以減少實驗數(shù)量，取得更多成功。

Consider BenchSci, a Toronto-based company that seeks to speed the drug development process. It aims to make it easier for scientists to find needles in haystacks—to zero in on the most crucial information embedded in pharma companies’ internal databases and in the vast wealth of published scientific research. To get a new drug candidate into clinical trials, scientists must run costly and time-consuming experiments. BenchSci realized that scientists could conduct fewer of these—and achieve greater success—if they applied better insights from the huge number of experiments that had already been run.

以上文字選自《哈佛商業(yè)評論》中文版2020年11月刊《機器學習的制勝之道》

阿杰伊·阿格拉沃爾（Ajay Agrawal）約書亞·甘斯（Joshua Gans）阿維·戈德法布（Avi Goldfarb）丨文

馬冰侖?丨編輯