Geoffrey Hinton 杰弗里·辛顿

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Geoffrey Hinton杰弗里·辛顿
CC FRS FRSC
Hinton in 20232023年的Hinton
Born出生Geoffrey Everest Hinton杰弗里·埃弗雷斯特·辛顿
6 December 1947[11]
1947年12月6日(75岁) [11]
Wimbledon, London, England
英格兰,伦敦,温布尔登
Education教育
Known for以知名
Awards奖项
Scientific career科学事业
Fields领域
Institutions机构
Thesis论文Relaxation and its role in vision (1977)
放松及其在视觉中的作用(1977)
Doctoral advisor博士导师Christopher Longuet-Higgins[2][3][4]
克里斯托弗·朗格特-希金斯 [2] [3] [4]
Doctoral students博士研究生
Other notable students其他值得注意的学生
Website网站www.cs.toronto.edu/~hinton/

Geoffrey Everest Hinton CC FRS FRSC[12] (born 6 December 1947) is a British-Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. From 2013 to 2023, he divided his time working for Google (Google Brain) and the University of Toronto, before publicly announcing his departure from Google in May 2023, citing concerns about the risks of artificial intelligence (AI) technology.[13] In 2017, he co-founded and became the chief scientific advisor of the Vector Institute in Toronto.[14][15]
杰弗里·埃弗里斯特·辛顿 CC FRS FRSC [12] (1947年12月6日出生)是一位英国-加拿大认知心理学家和计算机科学家,他在人工神经网络方面的工作最为人所知。从2013年到2023年,他在谷歌(Google Brain)和多伦多大学之间分配他的工作时间,直到2023年5月公开宣布离开谷歌,引发了对人工智能(AI)技术风险的担忧。 [13] 2017年,他共同创立并成为多伦多Vector研究所的首席科学顾问。 [14] [15]

With David Rumelhart and Ronald J. Williams, Hinton was co-author of a highly cited paper published in 1986 that popularised the backpropagation algorithm for training multi-layer neural networks,[16] although they were not the first to propose the approach.[17] Hinton is viewed as a leading figure in the deep learning community.[18][19][20][21][22] The dramatic image-recognition milestone of the AlexNet designed in collaboration with his students Alex Krizhevsky[23] and Ilya Sutskever for the ImageNet challenge 2012[24] was a breakthrough in the field of computer vision.[25]
与David Rumelhart和Ronald J. Williams一起,Hinton是1986年发表的一篇高度引用的论文的合著者,该论文推广了用于训练多层神经网络的反向传播算法,尽管他们并非首先提出这种方法的人。Hinton被视为深度学习社区的领军人物。他与学生Alex Krizhevsky和Ilya Sutskever合作设计的AlexNet在2012年的ImageNet挑战中实现了显著的图像识别里程碑,这是计算机视觉领域的一次突破。

Hinton received the 2018 Turing Award (often referred to as the "Nobel Prize of Computing"), together with Yoshua Bengio and Yann LeCun, for their work on deep learning.[26] They are sometimes referred to as the "Godfathers of Deep Learning",[27][28] and have continued to give public talks together.[29][30]
Hinton与Yoshua Bengio和Yann LeCun一起获得了2018年的图灵奖(通常被称为“计算机的诺贝尔奖”),以表彰他们在深度学习方面的工作。他们有时被称为“深度学习的教父”,并继续一起进行公开演讲。

In May 2023, Hinton announced his resignation from Google to be able to "freely speak out about the risks of A.I."[31] He has voiced concerns about deliberate misuse by malicious actors, technological unemployment, and existential risk from artificial general intelligence.[32]
2023年5月,Hinton宣布从Google辞职,以便能够"自由地谈论人工智能的风险"。 [31] 他对恶意行为者的故意滥用、技术失业以及来自人工通用智能的存在风险表达了担忧。 [32]

Education教育

Hinton was educated at King's College, Cambridge. After repeatedly changing his degree between different subjects like natural sciences, history of art, and philosophy, he eventually graduated in 1970 with a bachelor of arts in experimental psychology.[11] He continued his study at the University of Edinburgh where he was awarded a PhD in artificial intelligence in 1978 for research supervised by Christopher Longuet-Higgins.[2][33]
Hinton在剑桥大学的国王学院接受教育。在反复更改他的学位,从自然科学、艺术史和哲学等不同的学科,他最终在1970年以实验心理学的艺术学士学位毕业。 [11] 他在爱丁堡大学继续他的研究,在1978年由Christopher Longuet-Higgins指导的研究中获得了人工智能的博士学位。 [2] [33]

Career and research职业和研究

After his PhD, Hinton worked at the University of Sussex and, (after difficulty finding funding in Britain),[34] the University of California, San Diego and Carnegie Mellon University.[11] He was the founding director of the Gatsby Charitable Foundation Computational Neuroscience Unit at University College London[11] and is currently[35] a professor in the computer science department at the University of Toronto. He holds a Canada Research Chair in Machine Learning and is currently an advisor for the Learning in Machines & Brains program at the Canadian Institute for Advanced Research. Hinton taught a free online course on Neural Networks on the education platform Coursera in 2012.[36] He joined Google in March 2013 when his company, DNNresearch Inc., was acquired, and was at that time planning to "divide his time between his university research and his work at Google".[37]
在获得博士学位后,Hinton在苏塞克斯大学工作,(在英国找不到资金后),他在加利福尼亚大学圣地亚哥分校和卡内基梅隆大学工作。他是伦敦大学学院Gatsby慈善基金会计算神经科学单位的创始主任,目前在多伦多大学计算机科学系担任教授。他担任加拿大研究机构机器学习研究主席,并目前是加拿大先进研究所机器与大脑学习项目的顾问。2012年,Hinton在Coursera教育平台上教授了一个关于神经网络的免费在线课程。他在2013年3月加入了Google,当时他的公司DNNresearch Inc.被收购,他当时计划“在大学研究和Google的工作之间分配他的时间”。

Hinton's research concerns ways of using neural networks for machine learning, memory, perception, and symbol processing. He has written or co-written more than 200 peer reviewed publications.[1][38] At the Conference on Neural Information Processing Systems (NeurIPS) he introduced a new learning algorithm for neural networks that he calls the "Forward-Forward" algorithm. The idea of the new algorithm is to replace the traditional forward-backward passes of backpropagation with two forward passes, one with positive (i.e. real) data and the other with negative data that could be generated solely by the network.[39]
Hinton的研究涉及使用神经网络进行机器学习、记忆、感知和符号处理的方法。他已经撰写或合写了200多篇同行评审的出版物。在神经信息处理系统(NeurIPS)会议上,他介绍了一种他称之为“前向-前向”算法的新的神经网络学习算法。这个新算法的想法是用两个前向传递来替换反向传播的传统前向-后向传递,一个是正(即真实)数据,另一个是网络可以单独生成的负数据。

While Hinton was a postdoc at UC San Diego, David E. Rumelhart and Hinton and Ronald J. Williams applied the backpropagation algorithm to multi-layer neural networks. Their experiments showed that such networks can learn useful internal representations of data.[16] In an interview of 2018,[40] Hinton said that "David E. Rumelhart came up with the basic idea of backpropagation, so it's his invention". Although this work was important in popularising backpropagation, it was not the first to suggest the approach.[17] Reverse-mode automatic differentiation, of which backpropagation is a special case, was proposed by Seppo Linnainmaa in 1970, and Paul Werbos proposed to use it to train neural networks in 1974.[17]
在Hinton在加州大学圣地亚哥分校做博士后期间,David E. Rumelhart、Hinton和Ronald J. Williams将反向传播算法应用于多层神经网络。他们的实验表明,这种网络可以学习数据的有用内部表示。 [16] 在2018年的一次采访中, [40] Hinton说:“David E. Rumelhart提出了反向传播的基本思想,所以这是他的发明”。尽管这项工作在推广反向传播方面很重要,但它并不是首次提出这种方法。 [17] 反向模式自动微分,反向传播是其特例,由Seppo Linnainmaa在1970年提出,Paul Werbos在1974年提出将其用于训练神经网络。 [17]

During the same period, Hinton co-invented Boltzmann machines with David Ackley and Terry Sejnowski.[41] His other contributions to neural network research include distributed representations, time delay neural network, mixtures of experts, Helmholtz machines and Product of Experts. In 2007, Hinton coauthored an unsupervised learning paper titled Unsupervised learning of image transformations.[42] An accessible introduction to Geoffrey Hinton's research can be found in his articles in Scientific American in September 1992 and October 1993.[43]
在同一时期,Hinton与David Ackley和Terry Sejnowski共同发明了玻尔兹曼机。 [41] 他对神经网络研究的其他贡献包括分布式表示、时间延迟神经网络、专家混合、赫尔姆霍兹机和专家产品。2007年,Hinton共同撰写了一篇名为《无监督学习图像变换》的论文。 [42] 可以在1992年9月和1993年10月的《科学美国人》杂志中找到对Geoffrey Hinton研究的通俗介绍。 [43]

In October and November 2017 respectively, Hinton published two open access research papers on the theme of capsule neural networks,[44][45] which according to Hinton, are "finally something that works well".[46]
在2017年的十月和十一月,Hinton分别发表了两篇关于胶囊神经网络主题的开放获取研究论文, [44] [45] 据Hinton说,这些是"最终有效的东西"。 [46]

In May 2023, Hinton publicly announced his resignation from Google. He explained his decision by saying that he wanted to "freely speak out about the risks of A.I." and added that a part of him now regrets his life's work.[13][31]
在2023年5月,Hinton公开宣布从Google辞职。他解释说,他做出这个决定是因为他想要“自由地谈论人工智能的风险”,并补充说,他现在对自己的一生工作有一部分的遗憾。 [13] [31]

Notable former PhD students and postdoctoral researchers from his group include Peter Dayan,[47] Sam Roweis,[47] Max Welling,[47] Richard Zemel,[2][5] Brendan Frey,[6] Radford M. Neal,[7] Yee Whye Teh,[8] Ruslan Salakhutdinov,[9] Ilya Sutskever,[10] Yann LeCun,[48] Alex Graves,[47] and Zoubin Ghahramani.
他的团队中曾经的博士生和博士后研究员包括Peter Dayan, [47] Sam Roweis, [47] Max Welling, [47] Richard Zemel, [2] [5] Brendan Frey, [6] Radford M. Neal, [7] Yee Whye Teh, [8] Ruslan Salakhutdinov, [9] Ilya Sutskever, [10] Yann LeCun, [48] Alex Graves, [47] 和 Zoubin Ghahramani。

Honours and awards荣誉和奖项

In 2016, from left to right,
在2016年,从左到右,
Russ Salakhutdinov, Richard S. Sutton, Geoffrey Hinton, Yoshua Bengio, and Steve Jurvetson
Russ Salakhutdinov, Richard S. Sutton, Geoffrey Hinton, Yoshua Bengio, 和 Steve Jurvetson

Hinton was elected a Fellow of the Royal Society (FRS) in 1998.[12] He was the first winner of the Rumelhart Prize in 2001.[49] His certificate of election for the Royal Society reads:
1998年,辛顿被选为皇家学会(FRS)的会员。 [12] 他是2001年Rumelhart奖的首位获得者。 [49] 他的皇家学会选举证书上写道:

Geoffrey E. Hinton is internationally distinguished for his work on artificial neural nets, especially how they can be designed to learn without the aid of a human teacher. This may well be the start of autonomous intelligent brain-like machines. He has compared effects of brain damage with effects of losses in such a net, and found striking similarities with human impairment, such as for recognition of names and losses of categorisation. His work includes studies of mental imagery, and inventing puzzles for testing originality and creative intelligence. It is conceptual, mathematically sophisticated, and experimental. He brings these skills together with striking effect to produce important work of great interest.[50]
杰弗里·E·辛顿因其在人工神经网络方面的工作而享有国际声誉,尤其是他们如何能在没有人类教师的帮助下进行设计学习。这可能是自主智能类似大脑的机器的开始。他比较了大脑损伤的效果和这种网络中的损失,发现了与人类损伤,如名字识别和分类损失的惊人相似性。他的工作包括对心理意象的研究,以及发明用于测试创新性和创造性智能的难题。它是概念性的,数学上精细的,和实验性的。他将这些技能巧妙地结合在一起,产生了极大的兴趣的重要工作。 [50]

In 2001, Hinton was awarded an honorary doctorate from the University of Edinburgh.[51] He was the 2005 recipient of the IJCAI Award for Research Excellence lifetime-achievement award.[52] He has also been awarded the 2011 Herzberg Canada Gold Medal for Science and Engineering.[53] In 2013, Hinton was awarded an honorary doctorate from the Université de Sherbrooke.[54]
2001年,辛顿获得了爱丁堡大学的荣誉博士学位。 [51] 他是2005年IJCAI研究卓越终身成就奖的获得者。 [52] 他还获得了2011年的赫兹伯格加拿大科学与工程金奖。 [53] 2013年,辛顿获得了谢布鲁克大学的荣誉博士学位。 [54]

In 2016, he was elected a foreign member of National Academy of Engineering "for contributions to the theory and practice of artificial neural networks and their application to speech recognition and computer vision".[55] He also received the 2016 IEEE/RSE Wolfson James Clerk Maxwell Award.[56]
2016年,他因对人工神经网络的理论和实践以及其在语音识别和计算机视觉中的应用,被选为国家工程院的外籍成员。 [55] 他还获得了2016年的IEEE/RSE沃尔夫森詹姆斯·克拉克·麦克斯韦奖。 [56]

He has won the BBVA Foundation Frontiers of Knowledge Award (2016) in the Information and Communication Technologies category "for his pioneering and highly influential work" to endow machines with the ability to learn.[57]
他在信息和通信技术类别中赢得了BBVA基金会知识前沿奖(2016),“因为他开创性和极具影响力的工作”赋予了机器学习的能力。 [57]

Together with Yann LeCun, and Yoshua Bengio, Hinton won the 2018 Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.[58][59][60]
Hinton与Yann LeCun和Yoshua Bengio一起,因为在概念和工程上的突破使深度神经网络成为计算的关键组成部分,赢得了2018年的图灵奖。 [58] [59] [60]

In 2018, he became a Companion of the Order of Canada.[61] In 2021, he received the Dickson Prize in Science from the Carnegie Mellon University[62] and in 2022 the Princess of Asturias Award in the Scientific Research category, along with Yann LeCun, Yoshua Bengio, and Demis Hassabis.[63]
在2018年,他成为了加拿大勋章的伙伴。在2021年,他从卡内基梅隆大学获得了迪克森科学奖,而在2022年,他与Yann LeCun,Yoshua Bengio和Demis Hassabis一起获得了阿斯图里亚斯公主奖的科学研究类别奖。

Views观点

Risks of artificial intelligence
人工智能的风险

External videos外部视频
Geoffrey Hinton shares his thoughts on AI’s benefits and dangers, 60 Minutes YouTube video
Geoffrey Hinton 分享他对AI的好处和危险的看法,60分钟YouTube视频

See also: AI safety另见:AI安全

In 2023, Hinton expressed concerns about the rapid progress of rapid A.I.[32][31] Hinton previously believed that artificial general intelligence (AGI) was "30 to 50 years or even longer away."[31] However, in a March 2023 interview with CBS, he stated that "general-purpose AI" may be fewer than 20 years away and could bring about changes "comparable in scale with the Industrial Revolution or electricity."[32]
2023年,Hinton对快速发展的人工智能表示了担忧。 [32] [31] Hinton之前认为,人工通用智能(AGI)可能还需要"30到50年,甚至更长时间"。 [31] 然而,在2023年3月与CBS的一次采访中,他表示"通用人工智能"可能在20年内实现,并可能带来"与工业革命或电力相当的规模的变化"。 [32]

In an interview with The New York Times published on 1 May 2023,[31] Hinton announced his resignation from Google so he could "talk about the dangers of AI without considering how this impacts Google."[64] He noted that "a part of him now regrets his life's work" due to his concerns and he expressed fears about a race between Google and Microsoft.[31]
在2023年5月1日发表在《纽约时报》的一次采访中, [31] Hinton宣布他将从谷歌辞职,以便他可以“不考虑这如何影响谷歌地谈论AI的危险。” [64] 他指出,“他现在对他的一生的工作感到后悔的一部分”是因为他的担忧,他表达了对谷歌和微软之间的竞争的恐惧。 [31]

On early May 2023, Hinton revealed in an interview with BBC that AI might soon surpass the information capacity of the human brain. He described some of the risks posed by these chatbots as "quite scary". Hinton explained that chatbots have the ability to learn independently and share knowledge. This means that whenever one copy acquires new information, it is automatically disseminated to the entire group. This allows AI chatbots to have the capability to accumulate knowledge far beyond the capacity of any individual.[65]
2023年5月初,Hinton在接受BBC采访时透露,AI可能很快超过人脑的信息容量。他描述了这些聊天机器人带来的一些风险为“相当可怕”。Hinton解释说,聊天机器人有独立学习和分享知识的能力。这意味着每当一个副本获取新信息时,它会自动传播到整个群体。这使得AI聊天机器人有能力积累远超任何个体的知识。 [65]

Existential risk from AGI
来自AGI的存在性风险

Hinton expressed concerns about AI takeover, stating that "it's not inconceivable" that AI could "wipe out humanity."[32] Hinton states that AI systems capable of intelligent agency will be useful for military or economic purposes.[66] He worries that generally intelligent AI systems could "create sub-goals" that are unaligned with their programmers' interests.[67] He states that AI systems may become power-seeking or prevent themselves from being shut off, not because programmers intended them to, but because those sub-goals are useful for achieving later goals.[65] In particular, Hinton says "we have to think hard about how to control" AI systems capable of self-improvement.[68]
Hinton对AI接管表示了担忧,他说"这并非不可能",AI有可能"消灭人类"。 [32] Hinton表示,具有智能代理能力的AI系统将对军事或经济目的有用。 [66] 他担心,具有一般智能的AI系统可能"创建次级目标",这些目标与程序员的利益不一致。 [67] 他表示,AI系统可能会寻求权力或阻止自己被关闭,不是因为程序员希望他们这样做,而是因为这些次级目标对于实现后续目标有用。 [65] 特别是,Hinton说"我们必须深思熟虑如何控制"具有自我改进能力的AI系统。 [68]

Catastrophic misuse灾难性误用

Hinton worries about deliberate misuse of AI by malicious actors, stating that "it is hard to see how you can prevent the bad actors from using [AI] for bad things."[31] In 2017, Hinton called for an international ban on lethal autonomous weapons.[69]
Hinton担心恶意行为者故意滥用AI,他表示"很难看出你如何阻止坏人用[AI]做坏事。" [31] 2017年,Hinton呼吁国际禁止致命的自主武器。 [69]

Economic impacts经济影响

Hinton was previously optimistic about the economic effects of AI, noting in 2018 that: "The phrase 'artificial general intelligence' carries with it the implication that this sort of single robot is suddenly going to be smarter than you. I don't think it's going to be that. I think more and more of the routine things we do are going to be replaced by AI systems."[70] Hinton also previously argued that AGI won't make humans redundant: "[AI in the future is] going to know a lot about what you're probably going to want to do... But it's not going to replace you."[70]
Hinton之前对AI的经济效应持乐观态度,他在2018年指出:“‘人工通用智能’这个词带有这样的含义,即这种单一的机器人突然会比你更聪明。我不认为会是这样。我认为我们做的越来越多的常规事情将被AI系统取代。” [70] Hinton还曾经辩论过,AGI不会让人类变得多余:“[未来的AI]会对你可能想做的事情有很多了解...但它不会取代你。” [70]

In 2023, however, Hinton became "worried that AI technologies will in time upend the job market" and take away more than just "drudge work."[31]
然而,在2023年,Hinton变得“担心AI技术会随着时间的推移颠覆就业市场”,并且会带走的不仅仅是“乏味的工作”。 [31]

Politics政治

Hinton moved from the U.S. to Canada in part due to disillusionment with Ronald Reagan-era politics and disapproval of military funding of artificial intelligence.[34]
Hinton因为对罗纳德·里根时代的政治感到失望,以及不赞同军事资助人工智能,部分原因是从美国搬到了加拿大。 [34]

Personal life个人生活

Hinton's first wife, Rosalind Zalin, died of ovarian cancer in 1994.[71] His second wife, Jackie, died in April, 2023, also of cancer.
Hinton的第一任妻子,Rosalind Zalin,于1994年因卵巢癌去世。 [71] 他的第二任妻子,Jackie,也因癌症于2023年4月去世。

Hinton is the great-great-grandson of the mathematician and educator Mary Everest Boole and her husband, the logician George Boole,[72] whose work eventually became one of the foundations of modern computer science. Another great-great-grandfather of his was the surgeon and author James Hinton,[73] who was the father of the mathematician Charles Howard Hinton. Hinton's father was the entomologist Howard Hinton.[11][74] His middle name comes from another relative, George Everest, the Surveyor General of India after whom the mountain is named.[34] He is the nephew of the economist Colin Clark.[71]
Hinton是数学家和教育家Mary Everest Boole以及她的丈夫,逻辑学家George Boole的曾曾孙,他们的工作最终成为现代计算机科学的基础之一。他的另一位曾曾祖父是外科医生和作家James Hinton,他是数学家Charles Howard Hinton的父亲。Hinton的父亲是昆虫学家Howard Hinton。他的中间名字来自另一位亲戚George Everest,这位亲戚是印度的测量总监,珠穆朗玛峰就是以他的名字命名的。他是经济学家Colin Clark的侄子。

References参考资料

  1. ^ Jump up to: a b Geoffrey Hinton publications indexed by Google Scholar
    被Google Scholar索引的杰弗里·辛顿的出版物
  2. ^ Jump up to: a b c Geoffrey Hinton at the Mathematics Genealogy Project
    在数学家谱系项目中的杰弗里·辛顿
  3. ^ "Geoffrey E. Hinton's Academic Genealogy". Archived from the original on 23 March 2017. Retrieved 15 December 2013.
    "杰弗里·E·辛顿的学术谱系"。原文于2017年3月23日存档。检索于 15 December 2013年。
  4. ^ Gregory, R. L.; Murrell, J. N. (2006). "Hugh Christopher Longuet-Higgins. 11 April 1923 -- 27 March 2004: Elected FRS 1958". Biographical Memoirs of Fellows of the Royal Society. 52: 149–166. doi:10.1098/rsbm.2006.0012.
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