In today's world, Geoffrey Hinton has become a topic of increasing interest to a wide variety of people. With the advancement of technology and globalization, Geoffrey Hinton has taken a central role in different aspects of modern society. From its impact on the economy to its influence on culture and politics, Geoffrey Hinton has generated debates and discussions around its importance and repercussions. In this article, we will explore the various dimensions of Geoffrey Hinton, analyzing its implications and challenges in today's world. From its origins to its evolution today, Geoffrey Hinton has marked a turning point in the way we approach different aspects of contemporary life.
British-Canadian computer scientist and psychologist (born 1947)
With David Rumelhart and Ronald J. Williams, Hinton was co-author of a highly cited paper published in 1986 that popularized the backpropagation algorithm for training multi-layer neural networks, although they were not the first to propose the approach. Hinton is viewed as a leading figure in the deep learning community. The dramatic image-recognition milestone of the AlexNet designed in collaboration with his students Alex Krizhevsky and Ilya Sutskever for the ImageNet challenge 2012 was a breakthrough in the field of computer vision.
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. They are sometimes referred to as the "Godfathers of Deep Learning", and have continued to give public talks together.
He holds a Canada Research Chair in Machine Learning and is currently[when?] 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. 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".
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. 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.
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. In a 2018 interview, 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. 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.
In October and November 2017 respectively, Hinton published two open access research papers on the theme of capsule neural networks, which according to Hinton, are "finally something that works well".
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.
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.
He 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.
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.
In 2023, Hinton expressed concerns about the rapid progress of AI. Hinton previously believed that artificial general intelligence (AGI) was "30 to 50 years or even longer away." 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."
In an interview with The New York Times published on 1 May 2023, Hinton announced his resignation from Google so he could "talk about the dangers of AI without considering how this impacts Google." 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.
In 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.
Existential risk from AGI
Hinton expressed concerns about AI takeover, stating that "it's not inconceivable" that AI could "wipe out humanity." Hinton states that AI systems capable of intelligent agency will be useful for military or economic purposes. He worries that generally intelligent AI systems could "create sub-goals" that are unaligned with their programmers' interests. 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. In particular, Hinton says "we have to think hard about how to control" AI systems capable of self-improvement.
Catastrophic misuse
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 for bad things." In 2017, Hinton called for an international ban on lethal autonomous weapons.
Economic impacts
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." Hinton also previously argued that AGI won't make humans redundant: " going to know a lot about what you're probably going to want to do... But it's not going to replace you."
In 2023, however, Hinton became "worried that AI technologies will in time upend the job market" and take away more than just "drudge work."
Politics
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.
Personal life
Hinton's second wife, Rosalind Zalin, died of ovarian cancer in 1994; his third wife, Jackie, died in September 2018, also of cancer.
Hinton is the great-great-grandson of the mathematician and educator Mary Everest Boole and her husband, the logician George Boole, 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, who was the father of the mathematician Charles Howard Hinton.
^ abZemel, Richard Stanley (1994). A minimum description length framework for unsupervised learning (PhD thesis). University of Toronto. OCLC222081343. ProQuest304161918.
^ abFrey, Brendan John (1998). Bayesian networks for pattern classification, data compression, and channel coding (PhD thesis). University of Toronto. OCLC46557340. ProQuest304396112.
^ abNeal, Radford (1995). Bayesian learning for neural networks (PhD thesis). University of Toronto. OCLC46499792. ProQuest304260778.
^Krizhevsky, Alex; Sutskever, Ilya; Hinton, Geoffrey E. (3 December 2012). "ImageNet classification with deep convolutional neural networks". In F. Pereira; C. J. C. Burges; L. Bottou; K. Q. Weinberger (eds.). NIPS'12: Proceedings of the 25th International Conference on Neural Information Processing Systems. Vol. 1. Curran Associates. pp. 1097–1105. Archived from the original on 20 December 2019. Retrieved 13 March 2018.
^Hinton, Geoffrey E. (6 January 2020). "Curriculum Vitae"(PDF). University of Toronto: Department of Computer Science. Archived(PDF) from the original on 23 July 2020. Retrieved 30 November 2016.
Rothman, Joshua, "Metamorphosis: The godfather of A.I. thinks it's actually intelligent – and that scares him", The New Yorker, 20 November 2023, pp. 29–39.