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      "body": "![AI-co-keynote_imges.001.jpeg](https://cdn.steemitimages.com/DQmc688NmxZxGTYfUSRy8vodsVhiQtV5XsyuCBnRMNoBgtj/AI-co-keynote_imges.001.jpeg)\n\n这是 4 月做的一次分享。大概是我做过的分享中，现场讨论气氛最热烈的一次了。\n\n\n为什么分享这个话题呢？\n\n我做了好多年交互设计的工作，所以学习和工作一直有一个离不开的主题——人机交互（Human-Computer Interaction）。\n\n## 人机关系的演进\n\n什么是人机交互？我把它简单理解为：\n\n>人如何与机器或是某个复杂系统的沟通和互动过程。\n\n更为完整的版本，请看我在知乎上的回答 [什么是交互设计？](https://www.zhihu.com/question/19788122/answer/119319267)\n\n比如，鼠标就是一个解决人和机器互动问题的经典产品。\n\n![AI-co-keynote_imges.003.jpeg](https://cdn.steemitimages.com/DQmbxpfVbFugjfL4ySCNWkZJX12RRicPLYFACBccC7f6Aje/AI-co-keynote_imges.003.jpeg)\n\n现在大家都觉得鼠标再平常不过了，但是在当年，Douglas Engelbart 的这一项发明，极好地解决了人们在图形化计算机界面操纵屏幕元素的问题。为了表彰这项发明以及在人机交互领域的开拓式贡献，Douglas Engelbart 在 1997 年获得了「计算机界的诺贝尔奖」——图灵奖。\n\n> For an inspiring vision of the future of interactive computing and the invention of key technologies to help realize this vision. -- Turing Award Citation\n\n人和机器的对话、交互一直在演进。这也从一个侧面反映了人创造工具的历史：\n\n![](http://killerinfographics.com/wp-content/uploads/2014/12/DickiesIGjess.edits12-211.jpg)\n\n人创造的工具越来越多样、复杂、强大。想想看，今天我们在手机上能完成多少事情。\n\n于是，有人慢慢感到这种发展速度似乎不大对劲……奇点要来了，AlphaGo 也出现了。\n\n![human-progress.png](https://cdn.steemitimages.com/DQmcZh2MtJx7YVUfD7Cb5BwPcncMvuH3wifVtPSuHC1dikL/human-progress.png)\n\n随着深度学习热潮的演进，大家发现，以前为了让机器智能化而不得不做的很多工作，比如，符号化、标准化、模块化、输入大量专家知识、需要庞大的训练数据集、要做苦脏累的特征工程等等，现在都简化了。基本上，只要目标足够清晰、可操作，训练数据和计算资源足够，机器学习产生的结果就可以满足要求。\n\n我个人也经历了一次切身的体验。\n\n今年年初在上 Udacity 的 [Deep Learning Nano Degree 课程](https://github.com/kidult00/deep_learning_nanodegree_projects)的时候，最后一个实践项目，是基于人脸图像库，用生成对抗网络（GAN）算法自动生成人脸。\n\n虽然在学习过程中，早已对算法能实现的结果有所预期。但是当我把程序放到 AWS 的 GPU 上面跑了不到二十分钟，看到结果时还是忍不住隐隐感到机器的强大和压迫。\n\n这是最开始几个训练迭代生成的模糊不清的轮廓：\n\n![face-gen-before.png](https://cdn.steemitimages.com/DQmNuu5JSWZWPKT781wwP1JNq9J7y9qps5v2F44PQcKiNvS/face-gen-before.png)\n\n这是十几分钟训练结束后生成的人脸：\n\n![face-gen-after.png](https://cdn.steemitimages.com/DQmTj64q6W7CKfwS5BCtcrc8YMkYQpSB3C6kAq4wJyEvCh8/face-gen-after.png)\n\n\n说一点儿都不焦虑是假的。\n\n何况各种媒体还铺天盖地强化这种焦虑，今天是一百零八种会被机器取代的工作，明天是哪个比赛机器又战胜了人……\n\n于是大家经不住会问：\n\n![AI-co-keynote_imges.010.jpeg](https://cdn.steemitimages.com/DQmZvU6S9gGS8Mv6mWZ4psQDQEcHGKDXkq2oM4DvXhGrpeC/AI-co-keynote_imges.010.jpeg)\n\n似乎只有像诗词、小说、爵士乐、即兴表演这些领域，机器看起来就是不太擅长。\n\n但，也不好说。\n\nPaul 是一个专门给人画肖像画的机器。从 2011 年至今已经创做了非常多的作品。看起来非常有艺术感，不是吗？\n\n![AI-co-keynote_imges.011.jpeg](https://cdn.steemitimages.com/DQmWbkcrutaRvGVsCHo1Pjz9R2m3AE8xy9KeEevLgyvebdE/AI-co-keynote_imges.011.jpeg)\n\n\n\n有时候在焦虑之余，又忍不住会想：这种焦虑到底从何而来？\n\n如果看过莱考夫「我们赖以生存的隐喻」的同学都知道，隐喻 Metaphor 对我们的影响有多么大。仔细想想，感到机器的威胁，是不是也源于人机关系的一些基本隐喻？\n\n我们基本上都是以一种对立或一分为二的方式来看待这些基本词汇：\n\n![AI-co-keynote_imges.014.jpeg](https://cdn.steemitimages.com/DQmbyyQgXGnMdx1t2XHuATB8TjLXN1g6QiW2oZHtkuPXs6y/AI-co-keynote_imges.014.jpeg)\n\n看到这些词汇，我们会下意识地觉得它们是对立关系。逻辑、精确、冰冷的工程思维，怎么跟想象、感性、个性、创作融为一炉呢？\n\n这大概就是问题所在了。\n\n## 隐喻和可能\n\n语言有边界。这是语言能高效传递信息的代价之一。\n\n所以，打破语言或者说概念的界限，也许就是找到新出路的起点。\n\n（PS. 在心理咨询中，咨询师很重要的一部分工作，是帮助来访者呈现问题、reframe 问题，而不是直接插手去替他解决问题。）\n\n其实早在 50 多年前，就有人探讨过人机关系的「未来简史」。\n\n美国心理学家和计算机科学家 Licklider 在 Man-Computer Symbiosis 这篇论文中，讨论人跟机器的关系存在什么样的可能性，提出「人机共生」的概念。\n\n![AI-co-keynote_imges.016.jpeg](https://cdn.steemitimages.com/DQmYm8m9ATFeMu5wyBATcpMToertaZmkddQcyeyFEHndeJW/AI-co-keynote_imges.016.jpeg)\n\n快 60 年过去了，技术的发展让「蠢机器」第一次有可能成为人类的「好基友」，我们为什么首先感到的是焦虑而不是兴奋呢？\n\n还记得 2016 年，在 AlphaGo 战胜李世石后，跟安猪老师在大理有过一次讨论。当时我的感慨是，为 AlphaGo 的胜利感到挺开心的，但并不是因为我是技术的脑残粉，而是因为我觉得到了这个时候，人不得不去面对这样一个问题：\n\n> 人到底擅长什么？\n\n也就是说，当接受生物智能无法跟上非生物智能的发展时：\n\n![AI-co-keynote_imges.018.jpeg](https://cdn.steemitimages.com/DQmem7MjwYDo6cq2QigZmaTidA4SB748u3w3GJrmoCrMJS7/AI-co-keynote_imges.018.jpeg)\n\n人到底有哪些可以让机器「羡慕」（当然，机器不会有这种情绪）的天赋呢？\n\n至今我还没有答案，但我想，大概会包括：直觉、洞察力、探索和意外的创造。人欣赏「意外」的审美、运用「意外」的创造力，应该会长期秒杀机器。\n\n> 人跟机器最大的区别到底是什么？\n\n我一直在问自己这个问题。\n\n![AI-co-keynote_imges.021.jpeg](https://cdn.steemitimages.com/DQmTBgpkcgDZaL7x7oaP4xhX5NpQCzVbZSDNP2Hp8E42TBE/AI-co-keynote_imges.021.jpeg)\n\n意图，可能是现阶段人跟机器最大的差别。\n\n人因为时间、空间、认知方面的三大局限，因为生命时长极其有限、可触达的空间极其有限、工作记忆（内存）极其有限，决定了人类的一些基本 drive 和价值观。人的美妙与荒谬，可能都来自于两行二选一的核心代码：\n\n> Be numb/sober and Duplicate\n> \n> Be insane/self-distroying and Love\n\n\n所以我（naive地）认为，突破时间、空间、认知限制，渴望无限，是人类物种永恒的追求，换一个词来概括，那就是——意图。\n\n我们在基本意图上发展出复杂的能力，会体会微妙的情感，会追求意义，会审美、表达、共情，会讲故事和构建社会关系……\n\n而机器，无所不能，但就是没有意图！\n\n**这样看来，现在难道不是人类最好的时代吗？有一个毫无私心、任劳任怨、潜力无限的助手，为什么我们担心的是被助手取代，而不是兴高采烈地放开手脚去创造呢？**\n\n## 人机关系的新阶段\n\n无论技术如何发展，人类从来没有停止过创造。\n\n这是用 Google DeepDream 算法生成的作品：\n\n![DeepDream2.jpg](https://cdn.steemitimages.com/DQmWb5xSbEt518CehfwRkvvK2syadZ6gtzLCkN4bmqYiEke/DeepDream2.jpg)\n\nDeepDream 网站的首页，已经为人机关系的新时代做了注脚：\n\n![Human-ai-collaboration.png](https://cdn.steemitimages.com/DQmbNK81hKVJn3AHuXVHtubfMWtMJmecz9sRsppiGtFRPDt/Human-ai-collaboration.png)\n\n艺术家们一直在尝试探索人机关系，可能是这样的：\n\n![Alex Kiessling.jpg](https://cdn.steemitimages.com/DQmVvMvQoUbxZEPWkBNWpp4KJxaDwZTnk9P7XoeZRbsVLHM/Alex%20Kiessling.jpg)\n\n也可能是这样的：\n\n![di_2016_ars_11-1024x684.jpg](https://cdn.steemitimages.com/DQmUXNqBREgvbhcaaD8NWMdDgUMgm9cdnW5QWraLawKzfvU/di_2016_ars_11-1024x684.jpg)\n\n上图是一位叫做 Dragan iLiC 艺术家的项目，人充当机器的笔刷，由机械臂控制着人去作画。\n\n在分享的现场，这张图引发了热烈的讨论。有的小伙伴表示，这样做的目的到底是什么，她看不出任何意义。有的小伙伴认为这是对人机关系一种悲观的表达。有的小伙伴的态度则更为中立而开放……\n\n我想，这恰恰就是艺术家的目的吧：激发人们的反思与讨论。并不存在对与错，只是 raise questions，这也许就是最大的善意。\n\n## 音乐，人机协同即将爆发的领域？\n最近关注音乐和技术结合的领域比较多，越来越觉得音乐也许会迎来人机共创的繁荣时期。\n\n音乐本身是符号化、形式化非常强的「语言」。音乐的规律经过数百年的研究和沉淀，几乎已经没有太多规律上的「未知和秘密」，更多的变化会来自文化思潮影响下的风格探索。\n\n算法作曲早已不是什么新鲜事，基于 AI 自动生成音乐已经有很多成熟的应用。甚至是在对即兴表演要求相当高的爵士乐，也已经有人机共同即兴表演的尝试。比如 Al Biles 教授的 GenJam 项目，从 2005 年至今，已经演出过数十场。\n\n\n[GenJam's Journey_腾讯视频](https://v.qq.com/x/page/z0712pdmu7n.html)\n\n而去年的 Qosmo AI DJ 项目也展示了人类 DJ 和 AI DJ 一起 Jam 的效果~\n\n[AI DJ Project 人工智能 DJ 与人类合作打碟_腾讯视频](https://v.qq.com/x/page/t0602v8uxp2.html)\n\n## 何以解忧？\n\n> 这是最坏的时代\n> \n> 也是最好的时代\n\n这取决于我们现在每做的一个决定、每投入的分分秒秒。\n\n回到主题，如何破解 AI 时代的焦虑呢？\n\n以下是 00 给（自己）的解药：\n\n![AI-co-keynote_imges.031.jpeg](https://cdn.steemitimages.com/DQmcLLa4jnc1K1c1XEiXsWvNBdZkAobZuePF5DmtC7Vmp6N/AI-co-keynote_imges.031.jpeg)\n\n![AI-co-keynote_imges.032.jpeg](https://cdn.steemitimages.com/DQmbZYcg6iDaoveUkiAbSExaRfuVP74v1s6yZYp4PsS7wR8/AI-co-keynote_imges.032.jpeg)\n\n![AI-co-keynote_imges.033.jpeg](https://cdn.steemitimages.com/DQmfMrpr5zK77xoZsJGbsL85TBjRURW9ukadhTkeP7NHcRJ/AI-co-keynote_imges.033.jpeg)\n\n![AI-co-keynote_imges.035.jpeg](https://cdn.steemitimages.com/DQmT9mQ45Q25MHz22BsoE7pnaUmTvzterHNdsJSbJFng4WS/AI-co-keynote_imges.035.jpeg)\n\n回归创造者的使命，装备上创造所需要的心、眼、脑、手，去看去爱去体验。知识可以学习，但智慧更多是一种体验。把技术当成伙伴和切磋的对手，去行动，去试验，去创造。\n\n最后\n\n\n![AI-co-keynote_imges.037.jpeg](https://cdn.steemitimages.com/DQmZ2SKLAgbWsmtutmpkiecTWGuesZGBiyGCHCF9zqdSDKE/AI-co-keynote_imges.037.jpeg)\n\n![AI-co-keynote_imges.038.jpeg](https://cdn.steemitimages.com/DQmR515pnHQQtDueQ1HwqJNP3PHQdvGMwH7PEwyoNn47p2J/AI-co-keynote_imges.038.jpeg)\n\n\n## Ref\n- [Doug Engelbart 1968 Demo](https://web.stanford.edu/dept/SUL/library/extra4/sloan/MouseSite/1968Demo.html)\n- [PATRICK TRESSET |](http://patricktresset.com/new/)\n- [Licklider - Man-Computer Symbiosis.pdf](http://worrydream.com/refs/Licklider%20-%20Man-Computer%20Symbiosis.pdf)\n- [www.draganilic.org](http://www.draganilic.org/)\n- [GenJam](http://igm.rit.edu/~jabics/GenJam.html)",
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      "author": "kidult00",
      "body": "- [MakeNoise01 - 声音是什么？](https://www.uegeek.com/180621-make-noise-01.html)\n- [MakeNoise02 - 频率和音高](https://www.uegeek.com/180623-make-noise-02.html)\n- [MakeNoise03 - 振幅/响度/音量/增益 大乱斗](https://www.uegeek.com/180624-make-noise-03.html)\n- [MakeNoise04 - 波形和相位](https://www.uegeek.com/180626-make-noise-04.html)\n- [MakeNoise05 - Envelope 包络（ADSR）](https://www.uegeek.com/180628-make-noise-05.html)\n- [MakeNoise06 - 音色和均衡器(EQ)](https://www.uegeek.com/180629-make-noise-06.html)\n- [MakeNoise07 - 音频的分辨率 Sample rate/Bit depth](https://www.uegeek.com/180701-make-noise-07.html)\n- [MakeNoise08 - 振荡器和滤波器](https://www.uegeek.com/180703-make-noise-08.html)\n- [MakeNoise09 - 压缩 Compression](https://www.uegeek.com/180706-make-noise-09.html)\n\n\n音乐是时间的艺术。\n\n**时间也是信号的天然属性。而声音设计，同时还是空间的艺术。**\n\n声音必定产生在某个空间当中，人对声音的印象和记忆，多少都包含了对空间线索的直觉和感受。比如，密友之间的低语声，比较可能发生在一个小房间里；而轮船驶过的汽笛声，大概是在一个临海、开阔的码头，夹杂着繁忙的机械马达声和喧闹的人声。如果是清晨的港湾，大概又是不同的景象了。\n\n\n调整声音信号的方式有很多，延迟（delay）是必不可少的一种。混响（reverb）是塑造声音的空间感的秘密。今天我们就来一探究竟吧。\n\n## Delay 延时\n> Delay is an audio effect and an effects unit which records an input signal to an audio storage medium, and then plays it back after a period of time. — Wiki\n\n延时可以在时间上平移一个信号。延迟效果器储存并保留音频信号一段时间后再将其释放。\n\n![](https://wiki.audacityteam.org/w/images/5/53/Delay.png)\n\n延时是很多音效的核心，比如混响 Reverb、滤波 Filters 、和唱 Chorusing、Flanger（镶边，利用原音叠上稍微延迟的音，产生出金属感的回旋声）。\n\n通过控制多条延迟的声音，可以产生回声效果。以一定顺序衰减并释放每条延迟声音，可以模拟出真实的回声效果。\n\n延迟效果有很多种形式。比如在 Pink Floyd 的 [Comfortably Numb](https://open.spotify.com/track/5HNCy40Ni5BZJFw1TKzRsC) 里面，第一句歌词「Hello…Hello…Hello」是一个长延迟的效果。\n\n![](https://is4-ssl.mzstatic.com/image/thumb/Music49/v4/0f/f8/be/0ff8be03-465c-c0ce-4fe1-2db52ea50753/886445635850.jpg/1200x630bb.jpg)\n\n\nDelay 的参数：\n\n- Time/tap: the amount of time between the dry, unprocessed signal and the wet, processed signal.\n- Repeat/feedback: controls how many repeats of a delayed sound\n\n\n## Reverb 混响\n混响可以帮助我们营造合适的空间「场所感」。\n\n> 混响(reverberation)是声源发音停止后，声音继续存在的声学现象。—wiki\n\n声音在空间中传播，会遇到不同的物体，发生反射（reflection）、吸收（absorption）和扩散（diffusion）。混响产生于声波的传播被墙壁或周围障碍物反射而滞后于发声。\n\n![](http://www.prestigeacoustics.com.au/wp-content/uploads/2015/01/About-Sound5-copy3.png)\n\n不同的材料对声音的吸收效果不同，常常需要精密的计算和搭配，才能营造出理想的声音效果。\n\n![](https://www.acousticpanelsreview.com/wp-content/uploads/2014/09/Sound-Absorption-Coefficient.png)\n\n混响时间是指从其最初的反射声强度衰落 60 dB (降低到 -60dB)所花费的时间。\n\n![](https://upload.wikimedia.org/wikipedia/commons/thumb/6/6e/Reverberation_time_diagram.svg/440px-Reverberation_time_diagram.svg.png)\n\n延时时间的最小值，应该至少为混响时间的四分之一，而且延时的长度应为质数（或各延时时长是互质的）。合理的混响效果建议每秒有 1000 个回声。\n\n室内环境的声学效果涉及非常复杂的声学知识，尤其是录音棚和音乐厅。\n\n![](http://www.constructioncanada.net/wp-content/uploads/2016/02/Fig-4.jpg)\n\n下图是 00 所拍摄的上海交响乐团音乐厅的一角，舞台上方使用了很多木质材料，来获得最佳的混响和其他声音效果。\n\n![SSO.jpg](https://cdn.steemitimages.com/DQmdxNLzwd3WQiJ8jKv78C5FviWpLhfBzBvvEv8cKjywG1d/SSO.jpg)\n\n### 在 Ableton Live 里处理混响\n\n下面来看看 Ableton Live 里面是如何调整音频的混响的。\n\n混响的参数：\n\n- Dry/Wet: 没有混响/最大混响\n- Input: 控制对多少原始信号做混响处理\n- Reflect: 混响初期（early reflection），对混响感知影响更大\n- Diffuse: 混响末期（late reflection）\n- Size: 模拟不同大小的空间的混响 (对 Reflect 的影响大于 Diffuse)\n- Decay time: 混响消失所需时间\n- Predelay: 原始信号做混响处理之前的等待时间\n\n以下视频详细讲解了 Reverb 效果器的使用：\n\n\n[Reverb in Ableton Live_腾讯视频](https://v.qq.com/x/page/a0714gvoo1l.html)\n\n\n## Ref\n- [Audio Engineering 101 - A Beginner's Guide to Music Production](https://book.douban.com/subject/7069885/)\n- [Reverberation - Wikiwand](https://www.wikiwand.com/en/Reverberation)\n- [Acoustic Sound Absorption - Acoustic Panels Review](https://www.acousticpanelsreview.com/acoustic-sound-absorption/)\n- [Delay (audio effect) - Wikiwand](https://www.wikiwand.com/en/Delay_(audio_effect))\n- [Comb filter - Wikiwand](https://www.wikiwand.com/en/Comb_filter)\n- [混音指南（2016） | À quoi bon](http://aquoibon.li/mixing_guide/)",
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      "body": "你好吗？众所周知cn区两大众筹互助平台，@cnbuddy 和 @justyy ，股东，要不要了解一下？如果不想再收到我的留言，请回复“取消”。",
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      "body": "@kidult00, 生活，就是在平凡中变得不平凡！\n\n对了，漂漂女生节貌似快到了，我是不是要给 @cn-cutie.pie 可可 准备一下礼物了？",
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