zee

[zee @zeezy2002]

My shit post are a 1000% financial advice.If you are losing money with it,catch me if you can

[zee @zeezy2002]

However this migration is still in its early stages, and current prices have not yet fully reflected the logic of robotics as a foundational infrastructure.

[zee @zeezy2002]

Robots are likely undervalued now, seen as hardware rather than next gen AI infrastructure.
It isn't just an execution terminal, but a generator of real world data, simulation, control, and feedback loops. Its strategic position is far more foundational than realized.

[zee @zeezy2002]

To be more precise, it’s not that the market is completely unaware, but rather that those who see it have yet to reach a consensus on pricing.
You can already see capital shifting from pure computing power toward the underlying physical AI layers like chips, power etc

[zee @zeezy2002]

The real question is not whether they can, but under what conditions, to what quality, and whether they can generalize reliably and consistently.

[Zhu Liang @paradite_]

some good counter-arguments on why llms can generate novel ideas:

- rlvr (verifiable rewards) can encourage novel ideas given a good goal. i think this can work, but i am not sure if this can produce a model that generalize this capability to other domains without verifiable rewards. same logic for alpha go, alpha zero, they can do well in one domain, but can’t generalize.

- it depends on the question, if a new way to ask questions is discovered, it might trigger the model to give novel ideas (new model trajectory that was possible but unexplored). i think this is possible for very large models, but i’ve yet to this happen.

- combination or synthesis of existing ideas can produce novel ideas. i agree with this approach, but i don’t think it’s that simple. there’s more to it before one comes up with a genuinely novel idea, such as thinking deeply for days, talking to a friend, taking a walk, visiting places, getting a good sleep and other personal experiences. i don’t think current llms can mimic those human experiences yet.

also two more arguments against llms that emerged during the discussion:

- humans who came up with genuinely good ideas were often ridiculed and rejected by the society before. we can expect the same behavior during training where they were treated as part of loss during scoring, hence never make it to the final checkpoint.

- llms have all of human knowledge compressed in the weight, the knowledge can act as a barrier to new discoveries as the model defaults to applying existing knowledge and rules, instead of questioning them. but questioning existing rules is how genuinely novel ideas comes about.

thanks to everyone who participated in the discussion in good faith. i learnt a lot!

[zee @zeezy2002]

@hahagood @likev 本质上还是在单因果叙事，没办法解释现实中更复杂的全球经济结构

[zee @zeezy2002]

@hahagood 把一个复杂的全球经济系统过度简化成“美国单核驱动”的线性因果，这是站不住脚的。
发达国家在过去长期扮演需求和金融中心，并不意味着全球经济只能靠西方维持，更不意味着一旦西方衰退世界就必然瓦解😅

[zee @zeezy2002]

The alignment mechanism hides within microscopic, high-dimensional Calabi-Yau manifolds. The geometry of these extra dimensions dictates how strings vibrate, ultimately aligning and creating the speed of light, gravity, and spacetime itself.

[zee @zeezy2002]

AI training requires massive amounts of human text, essentially reverse engineering the laws of the universe through human lens. The real world is made of information, but even info needs a stable generative source. Where does this source align with reality?

[zee @zeezy2002]

If we decode how this source is constructed, humanity could build simulated universes. According to string theory, this "rendering engine" is 1D energy strings, generating matter & gravity via varying vibration frequencies (info).

[Sakana AI @SakanaAILabs]

The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature

Nature: https://t.co/nNfpSV5e5I
Blog: https://t.co/i6h8LVQOdl

When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle.

From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible.

Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process.

Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature!

This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement.

Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable.

Building upon our previous open-source releases (https://t.co/H1tBT14Yx8), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science.

This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team!

@_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune

[zee @zeezy2002]

智械启示录（Cybernetic Apocalypse）：AI 不是人类创造的工具，而是新物种的诞生。这不是灾难，而是演化的必然。

[zee @zeezy2002]

@lingboweibu319 加速主义的核心观点就是不应阻止 AI 发展，而应加速，因为抵抗无意义，唯一理性选择是拥抱即将到来的后人类时代。

[zee @zeezy2002]

@lingboweibu319 未来10-20年是对齐研究的关键期

[zee @zeezy2002]

DeepMind 在Language Modeling Is Compression那篇论文的一个观点简直太妙了：Transformer在语言上的成功，很大程度上是因为语言本身就是压缩后的世界模型

[zee @zeezy2002]

段永平建仓usdc母公司，什么意思，应该是想投资加密，但是得投个商业模式最最最稳得（泰达储备不透明和比特币etf有中间人，都信不过）
这就叫既要加密资产的红利，又要传统金融的保护伞

[zee @zeezy2002]

@otterpal24 @0xHACKETH 你是啥也不懂，打头会积累脑震荡，不论使劲打还是轻轻打。带护头是防止你眉弓颧骨被打开的，吸收不了拳头的能量