Geoffrey Hinton mbtiパーソナリティタイプ
個性
"Geoffrey Hintonはどのような性格タイプですか？ Geoffrey Hintonは、INTJ in MBTI、5w6 - - 514 in Enneagram、 in Big 5、LII in socionics のパーソナリティタイプです。"
I thought he was an INTP but now I think he's an INTJ. Here's why: His Ni is very obvious, he said in an interview that he was 7 when he knew that he'd have to get a PhD. When he was in high school a classmate of his told him about how the memories in a brain are divided around the brain and that got him obsessed with understanding the brains. This goal led him to study Physiology and Physics at university, when he understood that Physiology and Physics can't help him understand the brain he gave up and shifted to study Philosophy, he understood that even that won't help him so he shifted again to Psychology, and even after doing psychology he became hopeless and then became a carpenter and took some time off to think, and later he decided that AI is the way for him to go. This shifting of college major demonstrates Ni-Te very well, where Te is used to recalibrate and plan again to reach the Ni goal. When he joined the University of Edinburgh he worked on Neural Networks because to him that was the way, even though people in the AI community then had given up on Neural Nets and even his supervisor advised Hinton to focus on Symbolic AI rather than NNs as it was a dead-end. Nevertheless, Hinton still kept on working on NNs. The entire AI community focused on other methods and no one thought that NNs would even work, but Hinton never gave up on NNs and became the person to revolutionize AI with NNs. Again a great show of Ni and Te along with Fi. In an interview Hinton said that to him it was obvious from the start that NNs were the only way for AI to work. Furthermore he also said that the best way to understand something is to build one, which is a very Ni-Te approach. To me he seems like a pretty healthy INTJ.
バイオグラフィー
Geoffrey Everest Hinton (born 6 December 1947) is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto. In 2017, he cofounded and became the Chief Scientific Advisor of the Vector Institute in Toronto.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.
