报告人简介：李家康博士 剑桥大学博士毕业，美国国家工程院院士，广东康云多维视觉智能科技有限公司联台创始人，CEO兼 CTO 。中国图像图形学学会第一位外籍会员，华南理工大学校外硕士生导师，广东省图像图形学学会计算机视觉专业委员会副主任 ， 广东省图像图形学学会虚拟现实与智能交互专业委员会副主任。创办英国图形图像引擎公司，实现年收入 1 . 4 亿美金；个人拿下 Motorola 全球总订单5000万美金合同；发明专项70项。创立康云多维视觉智能科技有限公司，康云公司荣获2019年第八届中国创新创业大赛“人工智能与先进制造”成长组全国总冠军。 报告摘要：We already know that supervised models can perform extremely well on numerous tasks but the outstanding accuracy has its own price. Nevertheless, AI researches do a great job in creating more and more powerful mathematical models, the ones who are feeding these models often misuse them horribly or these models even aren’t meant to meet the expectations. Algorithms that work well in research phase can fail on totally unexpected problems. For example, it turned out that widely used today convolutional neural networks (CNNs) are suffering from adversarial attacks and tend to learn not the shape of the visual objects, but rather their texture (picture and the link above). The problem, probably, lies in the core mathematical operation chosen for the model — convolutions, that are not robust enough as they seem like. Generative AI will democratise 3D content creation and display in a similar manner to what has happened in other creative disciplines such as video production, photography, image processing and game development. At Light & Magic, we’re passionate about bringing these new “automation” tools to life and lessening the gap between idea and creation. We used GENERATIVE AI (embedded in hardware & software) for 3D Spatial Reality Computing. Providing ultra-fast scanning and mapping solutions for 3D measurement, documentation & realisation.