Artificial Intelligence: Million-dollar Babies

人工智能：百萬美元寶貝

As Silicon Valley fights for talent, universities struggle to hold on to their stars

硅谷搶奪人才，大學難留明星學者

That a computer program can repeatedly beat the world champion at Go, a complex board game, is a coup for the fast-moving field of artificial intelligence (AI). Another high-stakes game, however, is taking place behind the scenes, as firms compete to hire the smartest AI experts. Technology giants, including Google, Facebook, Microsoft and Baidu, are racing to expand their AI activities. Last year, they spent some $8.5 billion on research, deals and hiring, says Quid, a data firm. That was four times more than in 2010.考生如果怕自己錯過考試成績查詢的話，可以 免費預約短信提醒，屆時會以短信的方式提醒大家報名和考試時間。

計算機程序可以反復戰勝圍棋世界冠軍，這是人工智能這一快速發展的領域中一項極為難得的成就。然而，隨著各家公司競相把頂尖的人工智能老師招致麾下，另一場高風險游戲正在幕后展開。包括谷歌、Facebook、微軟、百度在內的科技巨頭爭相擴展其人工智能項目。數據公司Quid表示，去年，這些科技公司花費了約85億美元用于研究、收購及網羅人才，比2010年多四倍。

In the past universities employed the world’s best AI experts. Now tech firms are plundering departments of robotics and machine learning (where computers learn from data themselves) for the highest-flying faculty and students, luring them with big salaries similar to those fetched by professional athletes.

過去，大學擁有世界一流的人工智能老師。如今，科技企業正從大學的“機器人及機器學習(計算機通過數據自動學習)”系里搶奪優秀師生，以堪比職業運動員的高薪做誘餌。

Last year Uber, a taxi-hailing firm, recruited 40 of the 140 staff of the National Robotics Engineering Centre at Carnegie Mellon University, and set up a unit to work on self-driving cars. That drew headlines because Uber had earlier promised to fund research at the centre before deciding instead to peel off its staff. Other firms seek talent more quietly but just as doggedly. The migration to the private sector startles many academics. “I cannot even hold onto my grad students,” says Pedro Domingos, a professor at the University of Washington who specialises in machine learning and has himself had job offers from tech firms. “Companies are trying to hire them away before they graduate.”

美國卡耐基梅隆大學的國家機器人工程中心原本有140名教師，去年，打車公司優步從中招聘了40人，設立部門研究自動駕駛汽車。此舉惹來關注，因為優步之前承諾資助該中心的研究工作，后來卻轉而挖角。其他公司尋覓人才的舉動則相對低調，但也同樣執著。人才向私營公司的流動讓不少學者感到震驚。“我連自己的研究生也留不住，”華盛頓大學的佩德羅·多明戈斯教授說道，他是機器學習方面的老師，連他自己也收到了科技公司伸出的橄欖枝，“學生還沒畢業，那些公司就想把他們聘走。”

Experts in machine learning are most in demand. Big tech firms use it in many activities, from basic tasks such as spam-filtering and better targeting of online advertisements, to futuristic endeavours such as self-driving cars or scanning images to identify disease. As tech giants work on features such as virtual personal-assistant technology, to help users organise their lives, or tools to make it easier to search through photographs, they rely on advances in machine learning.

機器學習領域的老師最為搶手。大型科技公司的許多任務都要運用這一技術，從一些基本任務，如過濾垃圾郵件和令網絡廣告更有針對性，到無人駕駛汽車或掃描圖像來發現疾病等具有未來色彩的嘗試，無一例外。科技巨頭在研發一些產品時要依賴機器學習技術的進步，比如幫助用戶安排生活的虛擬個人助理或是方便人們搜尋圖片的工具。

Tech firms’ investment in this area helps to explain how a once-arcane academic gathering, the Conference on Neural Information Processing Systems, held each December in Canada, has become the Davos of AI. Participants go to learn, be seen and get courted by bosses looking for talent. Attendance has tripled since 2010, reaching 3,800 last year.

科技公司對這一領域的投資有助解釋為何“神經信息處理系統大會”(每年12月在加拿大舉行)這一曾被視為高深莫測的學術會議如今搖身成為人工智能界的達沃斯盛會。與會者一方面為了學習，另一方面也為了被求賢若渴的老板們發現并追捧。2010年以來，其與會人數增加了兩倍，去年達到3800人。

No reliable statistics exist to show how many academics are joining tech companies. But indications exist. In the field of “deep learning”, where computers draw insights from large data sets using methods similar to a human brain’s neural networks, the share of papers written by authors with some corporate affiliation is up sharply.

學術界有多少人轉投科技公司的懷抱目前仍無可靠統計數據，但有跡可循。“深度學習”是指計算機利用近似人類大腦神經網絡的運作方式從大型數據集中析取知識，這一范疇的學術論文中，在企業任職的作者比例大幅上升。

Tech firms have not always lavished such attention and resources on AI experts. The field was largely ignored and underfunded during the “AI winter” of the 1980s and 1990s, when fashionable approaches to AI failed to match their early promise. The present machine-learning boom began in earnest when Google started doing deals focused on AI. In 2014, for example, it bought DeepMind, the startup behind the computer’s victory in Go, from researchers in London. The price was rumoured to be around $600m. Around then Facebook, which also reportedly hoped to buy DeepMind, started a lab focused on artificial intelligence and hired an academic from New York University, Yann LeCun, to run it.

科技公司并非一開始就對人工智能老師傾注如此多的心思和資源。在上世紀八九十年代的“人工智能寒冬”，新潮的人工智能技術未如預期，該領域被廣為忽視，資金投入也不足。目前這股“機器學習”熱潮是在谷歌開始收購專注人工智能技術的公司后才真正開啟的。比如，2014年，谷歌從倫敦的研究人員手中收購了DeepMind，這家創業公司正是人機圍棋大戰中計算機取勝的幕后關鍵。據傳當時的收購價約為六億美元。據報道也曾有意收購DeepmMind的Facebook也在差不多同一時間建起實驗室，專注研發人工智能技術，并從紐約大學請來學者燕樂存來做負責人。

The firms offer academics the chance to see their ideas reach markets quickly, which many like. Private-sector jobs can also free academics from the uncertainty of securing research grants. Andrew Ng, who leads AI research for the Chinese internet giant Baidu and used to teach full-time at Stanford, says tech firms offer two especially appealing things: lots of computing power and large data sets. Both are essential for modern machine learning.

這些公司為學者們提供機會，讓其創意迅速推向市場，往往大受歡迎。私營公司的職位也令學者們不用擔心研究經費不足的問題。之前在斯坦福大學全職任教的吳恩達目前效力于中國互聯網巨頭百度，主管人工智能研究。他表示，科技公司能提供兩個特別誘人的條件：強大的計算能力和龐大的數據集。這兩者為現代機器學習研究必不可少。

All that is to the good, but the hiring spree could also impose costs. One is that universities, unable to offer competitive salaries, will be damaged if too many bright minds are either lured away permanently or distracted from the lecture hall by commitments to tech firms. Whole countries could suffer, too. Most big tech firms have their headquarters in America; places like Canada, whose universities have been at the forefront of AI development, could see little benefit if their brightest staff disappear to firms over the border, says Ajay Agrawal, a professor at the University of Toronto.

這些都是好的方面，但挖角熱潮也有代價。一方面，大學由于無法提供具有競爭力的薪酬，假如過多優秀人才被誘走，一去不返，或是忙于服務科技公司而無法專心講學，大學將蒙受損失。同時，一些國家也可能遭罪。大型科技公司總部多在美國;像加拿大這樣的國家，其大學一直處于人工智能研發的前沿，如果他們最聰明的人才都被境外公司吸引走，對本國實在毫無益處，多倫多大學的阿杰伊·阿格拉沃爾教授說道。

Another risk is if expertise in AI is concentrated disproportionately in a few firms. Tech companies make public some of their research through open sourcing. They also promise employees that they can write papers. In practice, however, many profitable findings are not shared. Some worry that Google, the leading firm in the field, could establish something close to an intellectual monopoly. Anthony Goldbloom of Kaggle, which runs data-science competitions that have resulted in promising academics being hired by firms, compares Google’s pre-eminence in AI to the concentration of talented scientists who laboured on the Manhattan Project, which produced America’s atom bomb.

另一風險是人工智能技術過度集中于少數企業手中。科技公司通過開源方式公開其部分研究成果。它們也答應員工可以撰寫論文。然而，實際上，許多有利可圖的研究成果并未共享。有人擔心，作為人工智能界領頭羊的谷歌可能形成近乎知識壟斷的地位。Kaggle是組織數據學競賽的平臺，不少公司通過這些比賽搜羅學術新星，該平臺的安東尼·古德魯姆將谷歌在人工智能上的卓越表現與當年集結眾多科學英才在曼哈頓計劃中努力工作相提并論。該計劃最終為美國造出原子彈。

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