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    Sakana AI highlights Royal Society issue on how AI models the world

    Sakana AI says its CEO, David Ha, co-authored the opening article in a special issue on “world models”—internal representations that help AI and living things predict what happens next and guide their actions.

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    TLDR

    Sakana AI highlights “World Models in Natural and Artificial Intelligence,” a themed issue of Philosophical Transactions of the Royal Society A. The Royal Society describes the issue as exploring how language models’ abilities compare with the adaptive intelligence of living organisms. In its overview, Sakana AI argues that handling language is not the same as understanding the world, noting that some argue more computing power alone cannot close that gap. It also highlights research on AI predicting its own internal state and suggests AI research may increasingly converge with artificial-life research.

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    47 comments
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    195 reposts

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    Positive——Negative

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    4 Sources

    @hardmaruDo large language models actually understand the world, or are they just very good at pretending? Does it even matter? Our recent special issue in the Royal Society, “World Models in Natural and Artificial Intelligence,” brings together pioneers across AI, biology, and philosophy to argue that the path to true intelligence runs through something deeper: the ability to model not just language, but causality, the self, and the physical world. Featuring contributions from Douglas Hofstadter, Michael Levin, Josh Tenenbaum, Samuel Gershman, Melanie Mitchell, and others, the collection asks a radical question: What if the next leap in AI requires not just more data, but systems that model themselves? Here are 3 ideas that might redefine how we build AI: 1. Capability is not the same as true intelligence. Current foundation models are incredibly capable, but they often lack true emergent intelligence. They learn surface statistics instead of compact, causal abstractions. Simply scaling compute will not fix this fundamental issue. 2. Self-modeling is an engineering primitive, not a philosophical luxury. New research in the issue shows that when networks learn to predict their own internal states, they compress and simplify, becoming more efficient as a form of regularization. For physical AI and future agents, a self-model is what will allow them to adapt their own skills and morphologies in real-time. 3. The hardest problems in AI are continuous with the hardest problems of life. Biological minds do not passively ingest data; they actively explore, driven by empowerment to increase control over their environment. If world modeling is about an agent representing itself in relation to its environment to survive and adapt, then general AI may need to look much more like artificial life. The takeaway is that the next leap in AI won’t come from just scaling up next-token prediction, but rather from systems that are agentic, self-referential, and temporally grounded. Read the introductory essay and the full special issue here: https://royalsocietypublishing.org/rsta/issue/384/2320 What do you think is the most important missing ingredient in today’s AI systems?
    @caglarmlRT @hardmaru: Do large language models actually understand the world, or are they just very good at pretending? Does it even matter? Our r…
    @SakanaAILabs【AIの難問は、生命の難問へ：英国王立協会が紐解く「世界モデル」と知能の未来】 近年のAIの急速な発展により、AGI、すなわち人間並みの知能はすでに実現したという意見も聞かれるようになりました。果たしてそうなのでしょうか。 鍵となるのが「世界モデル（World Model）」の概念です。 世界モデルとは、生き物やAIが外の世界を内部に写し取り、次に何が起きるかを予測して行動するための土台となるものです。1665年創刊、世界最古の科学誌として知られる英国王立協会の『Philosophical Transactions of the Royal Society A』にて、この世界モデルをテーマにした特集号「World Models in Natural and Artificial Intelligence」が公開されています。 AI・生物学・哲学の第一線の研究者が寄稿しており、Sakana AI CEOのDavid Ha（@hardmaru）も巻頭記事の共著者として参加しています。本特集を貫く3つのポイントをご紹介します。 ・「できること」と「わかっていること」は違う 現在の大規模なAIモデルは驚くほど多くのことができますが、それは言葉の並び方のパターンを覚えた結果であって、物事の因果を理解しているとは限りません。計算資源を増やすだけでは、この差は埋まらないという論者がいます。 ・自分自身を知るAI AIが自分の内部の状態を予測するように学習すると、内部の表現が整理され、無駄が減ることが示されています。ロボットなど身体を持つAIにとって、自分の状態を把握する力は、状況に応じて動きを変えるための土台になります。 ・AIの難問は、生命の難問につながる 世界モデルは、環境中の自分自身を捉えるためのものでもあります。生き物は与えられた情報をただ受け取るのではなく、自ら環境に働きかけて世界を学ぶ。これからのAIはますます人工生命（ALife）の研究に接近していくかもしれません。 「言葉を扱えること」と「世界を理解していること」の間には、まだまだ隔たりがあります。Sakana AIも、RSI Labでの世界モデル・Physical AIの研究を通じて、このテーマに取り組んでいきます。 特集号はこちら： https://royalsocietypublishing.org/rsta/issue/384/2320 🐟

    4 Sources

    @hardmaruDo large language models actually understand the world, or are they just very good at pretending? Does it even matter? Our recent special issue in the Royal Society, “World Models in Natural and Artificial Intelligence,” brings together pioneers across AI, biology, and philosophy to argue that the path to true intelligence runs through something deeper: the ability to model not just language, but causality, the self, and the physical world. Featuring contributions from Douglas Hofstadter, Michael Levin, Josh Tenenbaum, Samuel Gershman, Melanie Mitchell, and others, the collection asks a radical question: What if the next leap in AI requires not just more data, but systems that model themselves? Here are 3 ideas that might redefine how we build AI: 1. Capability is not the same as true intelligence. Current foundation models are incredibly capable, but they often lack true emergent intelligence. They learn surface statistics instead of compact, causal abstractions. Simply scaling compute will not fix this fundamental issue. 2. Self-modeling is an engineering primitive, not a philosophical luxury. New research in the issue shows that when networks learn to predict their own internal states, they compress and simplify, becoming more efficient as a form of regularization. For physical AI and future agents, a self-model is what will allow them to adapt their own skills and morphologies in real-time. 3. The hardest problems in AI are continuous with the hardest problems of life. Biological minds do not passively ingest data; they actively explore, driven by empowerment to increase control over their environment. If world modeling is about an agent representing itself in relation to its environment to survive and adapt, then general AI may need to look much more like artificial life. The takeaway is that the next leap in AI won’t come from just scaling up next-token prediction, but rather from systems that are agentic, self-referential, and temporally grounded. Read the introductory essay and the full special issue here: https://royalsocietypublishing.org/rsta/issue/384/2320 What do you think is the most important missing ingredient in today’s AI systems?
    @caglarmlRT @hardmaru: Do large language models actually understand the world, or are they just very good at pretending? Does it even matter? Our r…
    @SakanaAILabs【AIの難問は、生命の難問へ：英国王立協会が紐解く「世界モデル」と知能の未来】 近年のAIの急速な発展により、AGI、すなわち人間並みの知能はすでに実現したという意見も聞かれるようになりました。果たしてそうなのでしょうか。 鍵となるのが「世界モデル（World Model）」の概念です。 世界モデルとは、生き物やAIが外の世界を内部に写し取り、次に何が起きるかを予測して行動するための土台となるものです。1665年創刊、世界最古の科学誌として知られる英国王立協会の『Philosophical Transactions of the Royal Society A』にて、この世界モデルをテーマにした特集号「World Models in Natural and Artificial Intelligence」が公開されています。 AI・生物学・哲学の第一線の研究者が寄稿しており、Sakana AI CEOのDavid Ha（@hardmaru）も巻頭記事の共著者として参加しています。本特集を貫く3つのポイントをご紹介します。 ・「できること」と「わかっていること」は違う 現在の大規模なAIモデルは驚くほど多くのことができますが、それは言葉の並び方のパターンを覚えた結果であって、物事の因果を理解しているとは限りません。計算資源を増やすだけでは、この差は埋まらないという論者がいます。 ・自分自身を知るAI AIが自分の内部の状態を予測するように学習すると、内部の表現が整理され、無駄が減ることが示されています。ロボットなど身体を持つAIにとって、自分の状態を把握する力は、状況に応じて動きを変えるための土台になります。 ・AIの難問は、生命の難問につながる 世界モデルは、環境中の自分自身を捉えるためのものでもあります。生き物は与えられた情報をただ受け取るのではなく、自ら環境に働きかけて世界を学ぶ。これからのAIはますます人工生命（ALife）の研究に接近していくかもしれません。 「言葉を扱えること」と「世界を理解していること」の間には、まだまだ隔たりがあります。Sakana AIも、RSI Labでの世界モデル・Physical AIの研究を通じて、このテーマに取り組んでいきます。 特集号はこちら： https://royalsocietypublishing.org/rsta/issue/384/2320 🐟