朱雀 | SUZACQUE

There are a few notable counter-examples (e.g. HAL) but this is basically right for a lot of pre-2000 sci-fi. I think getting AI right from e.g. somewhere in the ~1970s would require a fairly complicated set of inferences, probably the biggest things would be: Image the world in 50 years. 1. "Digital knowledge work / computing". The vast majority of knowledge work (which makes up a very large fraction of the economy) goes digital. As computers get cheaper, much of society’s work happens on screens, over billions of machines in homes and offices. 2. "Internet". Similar to ARPANET (and see Licklider’s “Intergalactic Computer Network” (1963)), this creates demand for a global network of computers where people work, argue, teach, joke, and socialize. 3. "Big data". This creates a very large, compounding repository of digital information, with traces of how people reason, how they create, what they know etc. 4. "Statistical learning". Trying to program intelligence by hand is extremely difficult. But Shannon's n-gram approximations of English (1948) show examples of how language has statistical structure that can be "learned". You could imagine doing so over extensions of cybernetic computing (e.g. in the style of Rosenblatt's Perceptron 1958). So instead of programming thought, we could fit a statistical model to the record. 5. "LLMs". This might lead to something along the lines of ELIZA (1966), but with enough data (per 1-3), and enough compute (see Moore's law 1965), the imitation might become of extreme hight fidelity to the point that it's economically extremely useful. 6. "Scaling laws". These networks may demand a huge amount of data / computing resources and expertise to train. It's plausible that the more of each you have, the better your ELIZA gets. The economics concentrate, where a few organizations build a few minds and then deploy copies of them digitally. Something like that. Basically, it's a lot of leaps. You have to start with computers, to internet, to big data, to big compute (Moore's law), which then allow large-scale statistical learning approach based on neural networks, and then posit scaling laws, and then imagine the form factor of language models as a lot more sophisticated ELIZA. And you'd have to short everything else, most predominantly I think the idea that we'd figure out how to program robots by hand.

対話での賢さだけで言うとやっぱりGPT-6 Astra＞Fable5.1＞＞Opus 5.5だね。第一印象は間違ってなかった。 Sol？ 誰、それ？ BIGBANGの人かな？

発表されたばかりのGPT-6.1 Terra賢いな。理解力が高くて議論のしがいがある。

GPT-6 Astraの方がFable 5.1より少し賢いのかな？言わんとすることを理解してくれる度合いが違う。このプロンプトでどちらが賢いかお互いに評価させてみた：『建築と音楽は同じものとみなせる。フーリエ変換で連続と離散を切り替えるようなもの。』3者一致で僅差でAstraの勝利。

コーディングはAIで自動化できるようになったけど、マーケティングはまだまだ人間が自分でやる必要がある。Fable 5.5やGPT-6.1 Astraに期待しているのはマーケティングの自動化。その時のために今からAIに思う存分活躍してもらえる環境を構築しておいた方が良い。

AIエージェント時代に人間をボトルネックにしない方法 youtu.be/3oDOnRHbBy8?si=QAF5… @YouTubeより

2026年最大のイノベーション筆頭候補は当然Fable 5だけど、Omarchy（Linuxデスクトップ）もトップ3に入るかもしれない。 AstraはFableの後だし位置づけが難しいな。 Jev？まさか

YouTube動画を公開しました！ herdr：数十のAIエージェントを同時に動かす人はこんな画面にしている youtu.be/uoCWCUCN8P8?si=faQs… @YouTubeより

YouTube動画を公開しました！ herdr：数十のAIエージェントを同時に動かす人はこんな画面にしている youtu.be/uoCWCUCN8P8?si=faQs… @YouTubeより