“第八章 让人惊讶的智能”版本间的差异
来自cslt Wiki
（→演示链接） |
|||
| （相同用户的3个中间修订版本未显示） | |||
| 第19行： | 第19行： | ||
*计算机系孙茂松团队在自然·通讯杂志发表生物医学知识计算研究成果[https://www.cs.tsinghua.edu.cn/info/1088/4797.htm] | *计算机系孙茂松团队在自然·通讯杂志发表生物医学知识计算研究成果[https://www.cs.tsinghua.edu.cn/info/1088/4797.htm] | ||
*越来越像人：波士顿动力机器人进化史 [https://www.sohu.com/a/647095398_100051959] | *越来越像人：波士顿动力机器人进化史 [https://www.sohu.com/a/647095398_100051959] | ||
| + | *60年过去了，五指灵巧手还能走出实验室吗?[https://blog.csdn.net/zhixingjueqi/article/details/127447359] | ||
*无人机群体智能[https://www.toutiao.com/article/7119651267353412099/?wid=1658648374599][http://www.81.cn/jfjbmap/content/2021-09/03/content_298211.htm] | *无人机群体智能[https://www.toutiao.com/article/7119651267353412099/?wid=1658648374599][http://www.81.cn/jfjbmap/content/2021-09/03/content_298211.htm] | ||
*中国天眼[http://www.xinhuanet.com/politics/2021-04/26/c_1127375113.htm] | *中国天眼[http://www.xinhuanet.com/politics/2021-04/26/c_1127375113.htm] | ||
| 第40行： | 第41行： | ||
*Picsart[*] [https://picsart.com/ai-image-generator] | *Picsart[*] [https://picsart.com/ai-image-generator] | ||
*ChatGPT[*][https://chat.chatgptdemo.net/] | *ChatGPT[*][https://chat.chatgptdemo.net/] | ||
| + | *豆包(字节版ChatGPT) [http://doubao.com] | ||
| + | *天工（昆仑万维版ChatGPT）[http://tiangong.cn] | ||
| + | *OpenAI Blog, DALL·E: Creating Images from Text, 2021.1 [*](https://openai.com/blog/dall-e/) | ||
==开发者资源== | ==开发者资源== | ||
| 第54行： | 第58行： | ||
*Collaborative robots from Google [https://journals.sagepub.com/doi/pdf/10.1177/0278364917710318] | *Collaborative robots from Google [https://journals.sagepub.com/doi/pdf/10.1177/0278364917710318] | ||
*Deep learning for Muller mimickery [https://www.science.org/doi/pdf/10.1126/sciadv.aaw4967] | *Deep learning for Muller mimickery [https://www.science.org/doi/pdf/10.1126/sciadv.aaw4967] | ||
| − | |||
*AlphaFold [https://www.nature.com/articles/s41586-021-03819-2] | *AlphaFold [https://www.nature.com/articles/s41586-021-03819-2] | ||
*Robot make experiment[https://www.nature.com/articles/s41586-020-2442-2] | *Robot make experiment[https://www.nature.com/articles/s41586-020-2442-2] | ||
| + | *Zeng Z, Yao Y, Liu Z, et al. A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals. Nature communications, 2022, 13(1): 1-11.[https://www.nature.com/articles/s41467-022-28494-3] | ||
| + | *Levine S, Pastor P, Krizhevsky A, et al. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. The International journal of robotics research, 2018, 37(4-5): 421-436.[https://journals.sagepub.com/doi/full/10.1177/0278364917710318] | ||
| + | *Qiao C, Li D, Liu Y, et al. Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes. Nature biotechnology, 2022: 1-11.[https://www.nature.com/articles/s41587-022-01471-3] | ||
2023年8月25日 (五) 06:40的最后版本
教学资料
扩展阅读
- DeepMind AlphaGo博客[1]
- 百度百科:AlphaGo [2]
- AI100问：AI美颜[3]
- AI100问：绘画大师[4]
- AI100问：甜美的导航声音是如何产生的？[5]
- 老人声音报警器[6]
- DAll-E:文字生成图片[7][8]
- ChatGPT介绍[9][10]
- 计算机系孙茂松团队在自然·通讯杂志发表生物医学知识计算研究成果[11]
- 越来越像人：波士顿动力机器人进化史 [12]
- 60年过去了，五指灵巧手还能走出实验室吗?[13]
- 无人机群体智能[14][15]
- 中国天眼[16]
- AI超分辨率显微镜[17]
- AI100问：人工智能破解蛋白质结构之迷[18]
视频展示
- AI机器人做实验[19][20]
- 波士顿动力机器人[21][22]
- UC Berkley的科学家用一小时教会机器人站立、抓取等动作[23][24][25]
- CCTV-2 生活早间秀 除了下围棋 机器人还会作诗？[26]
- DALL-E2: [27]
演示链接
- Cogview 文本生成图片 [28]
- DALL-E 文本生成图片[29]
- Stable-difussion 文本图片生成 [30]
- ProsPainter：文本加绘图生成图片 [31]
- Artbreeder: 文本绘图 [32]
- TextToImage[*] [33]
- Picsart[*] [34]
- ChatGPT[*][35]
- 豆包(字节版ChatGPT) [36]
- 天工（昆仑万维版ChatGPT）[37]
- OpenAI Blog, DALL·E: Creating Images from Text, 2021.1 [*](https://openai.com/blog/dall-e/)
开发者资源
高级读者
- AlphaGo[38]
- AlhpaZero[39]
- FaceBeaty[40]
- Style transfer [41]
- A reivew of speech synthesis [42]
- Cogview [43]
- DALL-E [44][45]
- Collaborative robots from Google [46]
- Deep learning for Muller mimickery [47]
- AlphaFold [48]
- Robot make experiment[49]
- Zeng Z, Yao Y, Liu Z, et al. A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals. Nature communications, 2022, 13(1): 1-11.[50]
- Levine S, Pastor P, Krizhevsky A, et al. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. The International journal of robotics research, 2018, 37(4-5): 421-436.[51]
- Qiao C, Li D, Liu Y, et al. Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes. Nature biotechnology, 2022: 1-11.[52]
