This year’s Chinese new year (CNY) break is a little bit different for us. Because Beijing or the government in general discouraged people from returning home (due to COVID) and promoted “celebration in situ / 就地过年”, many people, including me, decided to postpone our home-returning plan.
I eventually put the time to some good reading, coding, and video clip watching. So here’s a short note of what I did.
I learned about CS193p from Hacker News one day before the break, and immediately decided to put it to my to-do list. I learned SwiftUI last year but since it was still in beta, I found myself unable to do many things as easily as in UIKit (maybe due to lack of good documentation), also the tooling sucked (had to switch between XCode 11 and 12 Beta, which was a huge pain when you had to download 20GiB of binaries and restart when failures).
The instructor Paul Hegarty, as mentioned in the comments, kept a very low profile on the Internet. But he indeed was an OG when it comes to Apple’s ecosystem (VP in NeXTStep?). I found his teaching style very sharp and enjoyable. He speaks very clearly and at a moderate pace. (I nevertheless turned on YouTube’s 1.75x speed because of my general familiarity of the material, which is also only possible exactly because of that.) The video recording of his live coding seems unbelievably fluent and natural, i.e. you can totally resonate even when he makes mistakes or typos, like the ones you would’ve made on your own, and that error exploration moment is gold, is when and how you actually learn. It’s like basically having a great coder being your teacher in your brain overseeing you translate thoughts to code.
I confirmed with a friend from Stanford that he was the same instructor during the iOS and UIKit era, which makes the popular course even more valuable. You can feel it when the instructor truly loves the subject and tries to keep up with the latest development: as of April 2020, he can fluently explain not only the best practices of SwiftUI during its beta phase, but also some quirks and bugs soon to be fixed. To this end, I’ve learned also what craftsmanship means.
After CS193p taken around 3–4 days, I’ve spent rest of the week on video clips. Chamanth gave a talk at GSP in 2017, where he explained some of his philosophical ideas and opinions on social networks and their impact on new generation. I found him a candid and controversial person, the latter of which isn’t necessarily a bad thing. I guess in the venture world, being an outlier is a gift or even a must.
The most point in his talk that resonated with me was that he saw money as a method or instrument of change, where one can amass in order to project his or her ideology and point of view to the world. Also he said too many fucks in that auditorium, I hope it’s fine with GSB.
There are some finished and ongoing readings that I’ve found interesting.
- Rand’s research on China’s grand strategy is really interesting in that a) I found translations like paramount leaders really amusing (and only after translation!), and b) the fact that this type of facility can really provide a view into China where you wouldn’t usually hear from Chinese materials and social circles
- 基于深度学习的生命科学 / Deep Learning for the Life Sciences from O’Reilly but the translation was really bad — seems like the translator didn’t even know the meaning of basic when presented along with acid, and then translated it into fundamental or primary in Chinese; nevertheless I see that materials are good and authors are really well informed, and it leads to my interest in Deeplearning.ai’s AI for medicine specialisations
Lastly I’ve been listening to conversations on Clubhouse on and off these days. However, due to its low information intensity I’ll usually just put my AirPods on while walking the dog. Most of the conversations are ill organized (but understandably so), while there were indeed some good ones.
Naval did one spontaneously where he discussed philosophical topics with people like Marc Andreessen, and to conclude he said that he only did this to be on par with Elon Musk, which he did, by reaching 5k listeners. He later explained in Twitter that he failed to properly record it because his iPhone was on vibrate. Nevertheless people uploaded the clips to YouTube and now it’s available in a very low efficiency format: unlike the video, you can’t preview and jump to a specific location on an audio clip.
I guess that’s an interesting problem to solve, since the technology i.e. TTS, is ready. It’s just the product in place.
这两天正好是有报道的新冠疫情一周年，Moderna 和 Pfizer 的疫苗已经进入冲刺阶段，基本达到 90% 以上的有效性，在英美已经开始申请加急许可了。
我一直在听 Dithering，这个付费播客的两个主播 John Grubber 和 Ben Thompson 都是科技界的大佬。上一期他们讨论到一个话题，就是体育运动员（比如橄榄球队成员）能不能优先打疫苗。Ben 说这是一个 no brainer：肯定是的。
Ben 重申了一下他的理由：按照现在疫苗预计的生产规模和速度，和几个大型赛事参赛队伍的规模，生产后者所需要的疫苗也就是几分钟的事情，不会耽误太多医务工作者的时间；医务工作者的确是很重要的，没有说他们不应该优先，但是假想体育运动员接种了疫苗之后有什么好处：第一，大型体育赛事至少可以不带观众的恢复，很多人因此可以在家看比赛有事做，起到居家隔离的效果；第二，很多运动员带头打疫苗，对于那些对疫苗持怀疑态度的人也有一个鼓励的作用。John 笑了笑说你其实可以不用解释这么多，因为我对我们的听众有信心。
大家对这个有没有判断力我不知道，至少愿意付费听他们播客的人肯定是筛选过的，应该是愿意听完 Ben 这么一番说道。但是如果是别的情况，我猜可能就没有那么简单了：这个小小的话题背后，其实是一个很深刻的话题：我们应该追求公平，还是效率？
最近在学习强化学习（资料有 Sutton & Barto 的书，Coursera 的课，以及 DeepMind 的课），其中一个很重要的概念是 discount factor γ，它指的大概是我们是如何在现在的 reward 和未来的预期 reward 之间获得取舍。（reward 这里的意思是对行为的回报，类似收益或者得分）。
别小看了这个 γ，比如很多强化学习处理的问题是 episodic game，比如围棋、走迷宫等等，有一个明确的起点和终点，结束了可以重来；但是很多现实的问题是没有终点的，我们需要在一个很长甚至无限的时间线上最大化收益（想起了有限与无限的游戏没有？）。处理这种无限时间的收益，必须有一个小于 1 的 discount factor，否则问题是不收敛的（当然另外一方面在 episodic game 里面可以把 γ 设成 1 就可以了）。如果 γ 越小，我们就越只顾眼前利益，我们的规划问题的算法就越「近视 myopic」；反之则看的越长远，但是相对来说收敛速度还有对计算资源的要求可能就会越高（因为要回顾的东西很多）。
但是这个 γ 很多时候是没有一个预设的值的，更多是一个「超参数」，也就是说需要经过多次实验，不断调整，才能找到一个合理高效的值。
一个简单的例子，机器人需要在左边和右边的路上做决策，γ 小就会走左边（活在当下），反之就会走右边（延迟满足），你甚至可以计算出 γ 的临界点。
Notion 链接是 https://www.notion.so/jiayul/Autonomous-Driving-Quick-Tour-3caaac13aa64430fa82f9f29b0660bfa
第二个是在 Air Reading Group 做了前几年读的一本书的阅读分享，叫做「Crossing the Chasm」。
Slides 链接是 https://crossing-the-chasm.now.sh/
周五（7月31日）参与了一个同事组织的对外线上分享活动，同时也在 B 站有直播。
讨论的话题是「技术人的职业发展和个人成长」，但是主要是从 meta-thinking 的角度来阐释的，因为我本身并不认为一个人可以为另外一个人的成长和发展给特别正确的结论——这个结论和决策是需要自己来亲力亲为的。
分享做的 Slides 我放到了 https://growth.jiayul.me ，以供之后参考用。