Group 1 - Geoffrey Hinton, known as the "father of AI," made his first public appearance in China at the WAIC 2025, sparking global attention and reflection on AI development [1] - Hinton's family background is deeply rooted in science, with connections to mathematics, physics, and agriculture, highlighting a legacy of scientific achievement [3][4] - Hinton's research journey began in the 1970s, focusing on artificial neural networks at a time when the field was largely overlooked, leading to significant breakthroughs in AI [6][7] Group 2 - The development of GPU technology in the early 2000s revitalized interest in neural networks, culminating in Hinton's pivotal work on backpropagation, which transformed machine learning [6][8] - In 2012, Hinton and his students developed AlexNet, winning the ImageNet competition and marking a turning point for deep learning as a core technology in AI [7][8] - Hinton has received both the Turing Award and the Nobel Prize in Physics, recognizing his contributions to deep learning and neural networks [8] Group 3 - Hinton has consistently raised alarms about the rapid advancement of AI, warning that it could surpass human intelligence and pose existential risks [10][11] - He emphasizes the need for a global AI safety collaboration mechanism and has criticized tech companies for prioritizing profits over regulation [11] - Hinton estimates a 10% to 20% probability that AI could take over and destroy human civilization, advocating for significant investment in AI safety research [11]

如果您希望可以时常见面，欢迎标星收藏哦~ 来源：内容来自计算机历史博物馆（CHM），谢谢。 计算机历史博物馆（CHM）与Google合作，发布了AlexNet 的源代码。AlexNet 是一个神经网 络，于 2012 年开启了当今流行的 AI 方法。该源代码可在CHM 的 GitHub 页面上以开源形式获 取。 什么是 AlexNet？ AlexNet 是 一 个 人 工 神 经 网 络 ， 用 于 识 别 照 片 内 容 。 它 由 当 时 的 多 伦 多 大 学 研 究 生 Alex Krizhevsky 和 Ilya Sutskever 以及他们的导师 Geoffrey Hinton 于 2012 年开发。 深度学习的起源 杰弗里·辛顿被认为是"深度学习"之父之一。深度学习是一种使用神经网络的人工智能，也是当今 主流人工智能的基础。上世纪 50 年代末，康奈尔大学研究员弗兰克·罗森布拉特首次构建了简单 的三层神经网络，其中只有一层自适应权重，但人们发现这种网络存在局限性。人们需要具有多层 自适应权重的网络，但没有很好的方法来训练它们。到 20 世纪 70 年代初，神经网络已被人工智 能研究人员普遍拒绝。 ...