警惕人工智能熱:AI離“下一個(gè)大事件”還遠(yuǎn)著呢
人工智能在過去幾年中取得了巨大的進(jìn)步,但對(duì)它當(dāng)前的功能,也有些宣傳過度的嫌疑。 這是上周五舊金山的一個(gè)專家座談會(huì)得出的結(jié)論。該座談會(huì)是由美國計(jì)算機(jī)學(xué)會(huì)在頒發(fā)第50屆圖靈獎(jiǎng)期間舉辦的,該獎(jiǎng)項(xiàng)主要獎(jiǎng)勵(lì)的是在計(jì)算機(jī)科學(xué)領(lǐng)域做出杰出貢獻(xiàn)者。 加州大學(xué)伯克利分校的機(jī)器學(xué)習(xí)專家、計(jì)算機(jī)科學(xué)教授邁克爾·喬丹表示,當(dāng)前人們對(duì)所謂“聊天機(jī)器人”的功能宣傳得有些過頭了。很多此類軟件使用的都是一種叫“深度學(xué)習(xí)”的技術(shù),人們會(huì)用海量的會(huì)話數(shù)據(jù)對(duì)它進(jìn)行“培訓(xùn)”,使它知道如何與真人進(jìn)行互動(dòng)。 盡管好幾家大型科技企業(yè)和創(chuàng)業(yè)公司都推出了能像真人一樣回答問題的聊天機(jī)器人,不過喬丹認(rèn)為,由于人類的語言極其復(fù)雜,機(jī)器人憑借深度學(xué)習(xí)等現(xiàn)有的技術(shù),依然無法完全掌握我們的語言。這些機(jī)器人本質(zhì)上其實(shí)是在?!吧缃皇滞蟆保麄兛梢葬槍?duì)特定語境做出寬泛的回應(yīng),但是它們“說不出關(guān)于現(xiàn)實(shí)世界的任何真實(shí)情況”。 喬丹表示:“我們進(jìn)入了一個(gè)機(jī)器學(xué)習(xí)被炒得火熱的時(shí)代?!睓C(jī)器學(xué)習(xí)雖然有改變整個(gè)經(jīng)濟(jì)的面貌的潛力,但“我們還沒到那個(gè)時(shí)候?!?/p> 會(huì)上,谷歌云機(jī)器學(xué)習(xí)首席科學(xué)家、斯坦福大學(xué)教授李飛飛表示:“我們生活在人工智能的一個(gè)最激動(dòng)人心、也是被炒得最火熱的時(shí)代?!崩铒w飛參與發(fā)起了ImageNet計(jì)算機(jī)視覺挑戰(zhàn)賽,這次挑戰(zhàn)賽又讓人工智能重新火了一把。參賽者要運(yùn)用機(jī)器學(xué)習(xí)技術(shù),在照片中識(shí)別諸如貓之類的物體。 李飛飛表示,雖然大家都在談?wù)揑mageNet的成功,“但我們很少討論失敗?!彼€強(qiáng)調(diào),為了制造出能像真人一樣“看”東西的計(jì)算機(jī),研究人員們付出了難以想象的艱辛。 不過李飛飛仍然信心十足地表示,目前人工智能領(lǐng)域已經(jīng)取得了不少具有里程碑意義的成就,最終它們將為我們帶來更多的突破,其影響會(huì)觸及諸如醫(yī)療保健等每一個(gè)行業(yè)。她表示:“我們正在進(jìn)入人工智能的一個(gè)新階段?!?/p> OpenAI公司是一家由伊隆·馬斯克注資的人工智能研究公司。該公司的研究總監(jiān)伊利婭·蘇特斯科娃指出,深度學(xué)習(xí)技術(shù)要想取得更多突破,首先取決于計(jì)算機(jī)硬件技術(shù)的持續(xù)發(fā)展,比如英偉達(dá)的GPU等等,如此才能確保人工智能系統(tǒng)以比以往更快的速度數(shù)據(jù)海量數(shù)據(jù)。深度學(xué)習(xí)技術(shù)將繼續(xù)與計(jì)算機(jī)硬件一道飛速發(fā)展,目前看來還沒有絲毫減緩的跡象。 蘇特斯科娃表示:“計(jì)算能力是深度學(xué)習(xí)技術(shù)的‘氧氣’。”(財(cái)富中文網(wǎng)) 譯者:樸成奎 |
Artificial intelligence has made great strides in the past few years, but it’s also generated much hype over its current capabilities. That’s one takeaway from a Friday panel in San Francisco involving leading AI experts hosted by the Association for Computing Machinery for its 50th annual Turing Award for advancements in computer science. Michael Jordan, a machine learning expert and computer science professor at University of California, Berkeley, said there is “way too much hype” regarding the capabilities of so-called chat bots. Many of these software programs use an AI technique called deep learning in which they are “trained” on massive amounts of conversation data so that they learn to interact with people. But despite several big tech companies and new startups promising powerful chat bots that speak like humans when prodded, Jordan believes the complexity of human language it too difficult for bots to master with modern techniques like deep learning. These bots essentially perform parlor tricks in which they respond with comments that are loosely related to a particular conversation, but they “can’t say anything true about the real world.” “We are in era of enormous hype of deep learning,” said Jordan. Deep learning has the potential to change the economy, he added, but “we are not there yet." Also in the panel, Fei-Fei Li, Google’s (goog, +0.89%) machine learning cloud chief and Stanford University Professor, said “We are living in one of the most exciting and hyped eras of AI.” Li helped build the ImageNet computer-vision contest, which spurred a renaissance in AI in which researchers applied deep learning to identify objects like cats in photos. But while everyone talks about ImageNet’s success, “we hardly talk about the failures,” she said, underscoring the hard work researchers have building powerful computers that can “see” like humans. Still, Li is excited that current AI milestones will eventually lead to more breakthroughs that will touch every single industry, like healthcare. “We are entering a new phase in AI,” she said. What will help usher more breakthroughs in deep learning will be the continuing advancements in powerful computing hardware, like Nvidia's GPUs that make it possible to crunch tremendous amounts of data faster than ever, explained Ilya Sutskever, the research director of Elon Musk-backed AI research group OpenAI. Deep learning will keep booming in tandem with advancements in computing hardware that shows no signs of slowing down. "Compute has been the oxygen of deep learning," Sutskever said. |
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