魏茨曼研究所Brain-IT模型可由脑扫描重建所见图像
以色列魏茨曼科学研究所教授Michal Irani团队开发出AI模型Brain-IT,可通过分析功能性磁共振成像(fMRI)数据重建受试者观看图像时的画面,并能反向根据给定图像预测大脑活动。该模型在公开的“自然场景数据集”(8名受试者、约7.3万组“图像+脑扫描”配对数据)上训练,并借助“反向翻译”模型扩充训练数据;团队称其在还原图像内容与细节方面优于已有模型,且只需约1小时新受试者的fMRI数据即可达到其他方法需数十小时训练才能实现的效果。研究者表示下一步将尝试解码听觉信息,长远目标包括视频乃至梦境,但强调这并非真正的“读心”——该技术不捕捉思想、记忆或语言,目前仍局限于实验室中的fMRI设备。
Israeli scientists have devised an AI model that can reconstruct and even predict what people are seeing with startling accuracy, likening the results to mind reading.
Their work still exists largely in lab settings, though the researchers are hopeful that advances down the line could be extended even as far as reading people’s dreams. More immediately, the technology could have potential for medical purposes, like helping people communicate who otherwise can’t due to injury or disability.
The AI model, called Brain-IT, was developed by professor Michal Irani and fellow researchers at the Weizmann Institute of Science in Israel. They’ve documented their work onGitHub and in a paper presented at a scientific conference earlier this year.
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Brain-IT takes advantage of the thing that AI is best at, which is pattern recognition. It takes in data from functional MRI brain scans, which track changes in blood flow and oxygen in the brain to measure brain activity, and then uses that data to recreate the images the person was thinking about when the scans took place.
#Brain-IT#Weizmann Institute of Science#Michal Irani#Natural Scenes Dataset#Kenneth Norman#Princeton University