科学
- Ignacio Grossmann的采访视频.Ignacio Grossmann是《Systematic Methods of Chemical Process Design》一书（天津大学化工过程分析与合成参考教材）的作者之一（另外两位是Biegler和Westerberg），在该视频中讲述了他的科研经历。
- Sang Yup Lee: Systems metabolic engineering for a green chemical industry: 韩国科学技术院(KAIST)李相玉教授介绍了从inherent/noinherent/creative三种方式开展合成生物学的学术报告，介绍了现有的如何强化、没有的如何设计（如PLA），以及生成无机量子点。还以BDO的合成为例说明不能marry Ecoli.
言论
- 自从有了 AI，我发现自己不再担心项目对我来说太大、太复杂，或者项目使用了我不了解的技术或编程语言，一切都变得容易得多。我正在重新审视一些我曾认为太复杂或超出我能力范围的业余项目，只要有时间，我就会去尝试。这是一个令人兴奋的时代。 – With AI You Need to Think Much Bigger!
- 我认为，数学本质上已经没有什么好问题了。让大量数学家感兴趣的问题数量每年都在减少，而且几乎所剩无几。现代数学研究越来越局限于少数人对某个特定主题的研究，即使是研究生也常常被现代数学问题的极端专业性和深奥性所困扰。未来的研究生不应再需要证明一些全新的东西，相反地，他们的主要目标可能是简化过去的研究结果. – Jason Polak:Is math running out of problems?
- 乔布斯说自己重新定义了手机，其实我们看下至今不像iphone的确实很难叫手机。 – 雷军2011在腾讯的演讲
- 键盘这种很难用的东西，已经毒害我们很久，但我们仍在强迫自己习惯它。 – 同上
- 我们觉得手机替代PC很痛苦，但这些痛点正是我们的机会。 – 同上
- 无论是spaceX还是Tesla，马斯克都看到了现有技术方案对未来发展的限制，认为从成本和技术前瞻性上都不具有优势，因此选择了可重复发射、仅依赖摄像头不依赖. – Elon Dreams and Bitter Lessons
- This is a big lesson. As a field, we still have not thoroughly learned it, as we are continuing to make the same kind of mistakes. To see this, and to effectively resist it, we have to understand the appeal of these mistakes. We have to learn the bitter lesson that building in how we think we think does not work in the long run. The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach. – Elon Dreams and Bitter Lessons
- One of the reasons why the computer can be so much better than a person is that we have millions of cars that are training on driving. It’s like living millions of lives simultaneously and seeing very unusal situations that a person in their entire lifetime would not see, hopefully. With that amount of training data, it’s obviously going to be much better than what a human could be, because you can’t live a million lives. It can also see in all directions simultaneously, and it doesn’t get tired or text or any of those things, so it will naturally be 10x, 20x, 30x safer than a human for all those reasons. – Elon Dreams and Bitter Lessons
- Musk has been over-promising and under-delivering in terms of self-driving for existing Tesla owners for years now, so the jury is very much out on whether current cars get full unsupervised autonomy. But that doesn’t change the fact that those cars do have cameras, and those cameras are capturing data and doing fine-tuning right now, at a scale that Waymo has no way of matching. – Elon Dreams and Bitter Lessons
- The Tesla bet, though, is that Waymo’s approach ultimately doesn’t scale and isn’t generalizable to true Level 5, while starting with the dream — true autonomy — leads Tesla down a better path of relying on nothing but AI, fueled by data and fine-tuning that you can only do if you already have millions of cars on the road. That is the connection to SpaceX and what happened this weekend: if you start with the dream, then understand the cost structure necessary to achieve that dream, you force yourself down the only path possible, forgoing easier solutions that don’t scale for fantastical ones that do. – Elon Dreams and Bitter Lessons
- LLMs, meanwhile, are commonly thought about in terms of language — it is in the name, after all — but what they actually predict are tokens, and tokens can be anything, including driving data. – Elon Dreams and Bitter Lessons
观点
Frank谈低代码编程和AI编程的发展
- 优秀的作品都是形式（form）和功能（function）的统一。形式必须服从功能，功能决定了形式，英文叫做”form follows function”。低代码编程的问题在于，它是先有 UI（形式），再有代码（功能）。用户先拖拉生成 UI，系统再根据 UI 生成代码。这是本末倒置，让底层代码适配 UI，注定了两者都有问题：UI 是空想出来的，代码为了适配 UI，注定冗余和低效。所以，优秀的软件不可能用这种方式生成，低代码编程不会成功。低代码编程解决不了这个根本缺陷，适用场景有限，大概只适合一些简单任务，或者生成原型，不会成为主流工具。程序员应该谨慎开发这类工具，付出的劳动很可能打水漂。 – Visual programming is stuck on the form
- AI 不同于低代码编程。低代码编程是使用者给出 UI，系统来生成代码，而 AI 是系统同时生成 UI 和代码，用户只需要说出需求即可。这种情况下，形式与功能的结合，完全取决于 AI 的能力。如果有一天，AI 视频能够成功，画面美，情节好，那么 AI 编程大概也会成功，生成形式与功能统一的应用程序。
100% Unemployment
我最近常常想一个问题：如果 AI 强大到所有方面都超过人类，它和机器人接管一切，人类要干什么呢？
凯文·凯利认为，随着工作都交给机器人，人类可以从事越来越多有趣的工作，就像工业革命后一样。
这种说法在短期内有一定道理，但是有一个前提，就是人类能做计算机做不到的事情。
我认为，没有理由认为这个前提会永远成立。
除非政府强制规定，计算机不得从事某些工作，只有人类可以做。但是那样的话，那些工作很可能就会停滞发展了。停滞发展的行业没有前景，收入也不会增长，从业者难以感到满意。
让我们假设一种极端的情况，如果机器完全超越人类，每件事都比人类做得好，大部分人无法为社会做出贡献时，一切会怎样？
如果一个人无法为社会做出贡献，也就失去了他的经济价值，就算他能靠政府的补助继续活着，那么对于他来说，个人价值是什么呢，就是活一天算一天？
目前来看，这个问题还比较遥远，就算那一天到来，也是很久以后的事情了。眼下比较现实的问题是，AI 正在大量减少高薪工作。随着机器的能力越来越强，很多白领工作的价值迅速变小，大多数人越来越难找到报酬丰厚、令人满意的工作。
这就是现在发生的问题，高薪的工作岗位不断减少，难以获得。
有趣
荐书
杂谈
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