MATH FOURNITY

数学元理论研究

With AI, Surpass AI

[MATH FOURNITY @math_fournity]

AI是人的放大器，也是人与人之间差距的放大器

[MATH FOURNITY @math_fournity]

1、在AI时代，我们不是不需要学习了，恰恰相反，有时多知道一个概念，可能你让AI做出来的东西，都会远远超过不知道这个概念的人。甚至，不知道的人，一直在用AI生产垃圾。

2、AI确实增强了我们个人的生产力，但是AI同时放大了我们和别人之间的认知差，拉大了人与人之间的交付力。

所以，在未来，人的信息获取能力和认知水平，某种意义上是被AI指数级放大了。

[MATH FOURNITY @math_fournity]

@mjjwiki 能细说吗？

[MATH FOURNITY @math_fournity]

@grok 中国2025高校毕业生数量

[Linda @wongyong_fan]

我觉得以后“大学生创业”可能都不是传统意义上的创业了。

不需要先租办公室， 也不需要一上来融资。

很可能就是：

宿舍里发现一个需求
用AI两天做个MVP
发到X上
100个人注册
10个人付钱

第一个月MRR只有几百刀。

但那一刻性质已经变了。

因为你第一次证明：

陌生人愿意为你做的东西付钱。

[MATH FOURNITY @math_fournity]

@Xmaxkoi 下个月不订了，不玩了

[MATH FOURNITY @math_fournity]

我的GPT订阅只剩最后不到12小时了，正在考虑入坑Opus 5.5

[MATH FOURNITY @math_fournity]

@AndyL5cc 千万不要，刚还搞出幺蛾子 🤣

[MATH FOURNITY @math_fournity]

@mjjwiki 不过确实，你在我的时间线上好久没有出现了

[MATH FOURNITY @math_fournity]

能打架的机器人和基因工程，或许就是AI带来的未来FATE的两个最重要的【标签】

[MATH FOURNITY @math_fournity]

我好像看明白了命运的齿轮：一个叫奥特曼，打架的。一个是生物学博士背景。

这两个组合在一起是什么故事？FATE

[MATH FOURNITY @math_fournity]

我好像看明白了命运的齿轮：一个叫奥特曼，打架的。一个是生物学博士背景。

这两个组合在一起是什么故事？FATE

[Dario Amodei @DarioAmodei]

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out.

It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend.

The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans).

More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains.

In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries.

Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology.

I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.

[MATH FOURNITY @math_fournity]

GLM 在工作的时候，有一个特点，我不知道我观察得对不对，它会倾向于在一个Thinking Spin中尽量想清楚更多的事情之后，再开始反馈或者做工具调用。

但是，你看GPT，它对于任何一个请求，现在做的第一件事都是Acknowledge用户意图，其实我想这可能代表了OpenAI后训练中的一个COT策略，就是把思考链，拆分，而不是一次完成尽量多的思考，再行动。

我为什么跟你们提这件事呢？因为GLM-5.2当初在我的做题系统中，经常在难题上，被打爆Thinking Spin，包括Fable 5，也是这样。

如果，AI学会了拆分COT，那么可能这种情况，就能够避免，一些本来完成不了的工作、“题目”——因为被打爆了Thinking Spin，通过这种自主的、有意识的COT拆分，或许就可以逐步推进了。

我觉得，如果我的这个观察是准确的，或许值得诸位注意一下这件事。

@Zai_org @Alibaba_Qwen @deepseek_ai @Kimi_Moonshot @MiniMax_AI @StepFun_ai @TencentHunyuan @AtriaASI @XiaomiMiMo

[MATH FOURNITY @math_fournity]

难得！在这个月的GPT订阅在最后不到24小时就截止的时间中，有了一个Session，让我觉得，我终于真的用上了传说中的Astra 🤣 @thsottiaux

[MATH FOURNITY @math_fournity]

Tibo @thsottiaux 好吧，它脑子又在线了

[MATH FOURNITY @math_fournity]

@AuricSource 形式化的理论的发展似乎有两个阶段：知性支撑的线性发展阶段和理性带来的非线性折返。每一次的非线性折返，都是向更高阶的跃迁的起点，也是下一波线性发展的起点。

[MATH FOURNITY @math_fournity]

@wawaup1024 我也想去试试

[MATH FOURNITY @math_fournity]

Tiktok会读取手机的基站链接和对周围基站的持续扫描情况，即便是美卡，当前是否漫游在中国的基站上？周围有没有中国的基站？Tiktok会据此进行用户的分区管理，如果它认为用户是中国人，那……可是会打击那些看不对的视频的用户的。而且打击是一个系列推送组：

1、首先让用户看党的宣传视频。
2、然后让用户看给俄乌战争中俄罗斯打气加油的视频。
3、最后，最精彩来了，给用户发激烈的撞车事故行车记录仪记录的最后几秒的激烈的第一视角视频，给用户的心理造成冲击、压力。

总之，就是先传宣，再打击，这个推送组过去之后，视频流再次回到正常。

[MATH FOURNITY @math_fournity]

Tiktok会读取手机的基站链接和对周围基站的持续扫描情况，即便是美卡，当前是否漫游在中国的基站上？周围有没有中国的基站？Tiktok会据此进行用户的分区管理，如果它认为用户是中国人，那……可是会打击那些看不对的视频的用户的。而且打击是一个系列推送组：

1、首先让用户看党的宣传视频。
2、然后让用户看给俄乌战争中俄罗斯打气加油的视频。
3、最后，最精彩来了，给用户发激烈的撞车事故行车记录仪记录的最后几秒的激烈的第一视角视频，给用户的心理造成冲击、压力。

总之，就是先传宣，再打击，这个推送组过去之后，视频流再次回到正常。

[MATH FOURNITY @math_fournity]

Tibo @thsottiaux 好吧，它脑子又在线了

[MATH FOURNITY @math_fournity]

我想了一下，新开Session，让AI甩掉了历史压缩中留下的“思想包袱”。因为即便经历了多次压缩，那些“不对”、“不可以”、“错误”肯定都是被持续地在压缩过程中保留的，而这些内容，成为了Astra正确思考的掣肘。

[MATH FOURNITY @math_fournity]

我想了一下，新开Session，让AI甩掉了历史压缩中留下的“思想包袱”。因为即便经历了多次压缩，那些“不对”、“不可以”、“错误”肯定都是被持续地在压缩过程中保留的，而这些内容，成为了Astra正确思考的掣肘。

[MATH FOURNITY @math_fournity]

泪目了 新开Session后 Astra的脑子回来了 😭

[MATH FOURNITY @math_fournity]

@chasen_liao 也可以考虑考虑自媒体公司，去见识见识成功的套路和接触更多行业资源

[MATH FOURNITY @math_fournity]

@chasen_liao 笑 Cry 😂