Absolutely fascinating work by @SakanaAILabs reproducing @kenneth0stanley Picbreeder in a non-interactive, VLM-agentic way. I've had years t…

Absolutely fascinating work by @SakanaAILabs reproducing @kenneth0stanley Picbreeder in a non-interactive, VLM-agentic way. I've had years to reflect on Kenneth Stanley's ideas as originally communicated in the book Why Greatness Cannot Be Planned. The most load-bearing term is "serendipity", and what that actually means. > We’re inspired by the idea that serendipity requires chance As we laid out in our creativity article, I think chance is actually the wrong way to think about serendipity. It's mostly about respecting constraints, and the chance, if there is any, happens in the degrees of freedom not covered by the constraints. I think open-endedness can be thought of in a privileged, symbolic/non-statistical way. The more recent work with Kenneth and @akarshkumar0101 elucidated to me that it's about three main things: First of all, the lack of any externalised agency ("following your own gradient of interest") or only non-complex internal agency or intelligence (as explained in his original book, framed there as "non-optimization"), secondly -- "deep understanding" (respecting constraints upstream in the phylogeny), or, in their terms "path dependence". And thirdly: "evolvability". It's also taken as a given that if the constraints are sparse/factorised/world-aligned, the more the better. Actually, the best possible sign of success of any representation learning architecture is sparsity, factorization, world-alignment and/or canalisation. I was able to breed the anthropomorphic images below by manually navigating an extremely deceptive search space over many, many iterations and starting with something that didn't look anything like a face. The reason Picbreeder works through interactive human supervision is that we understand the world at an abstract level, and we can easily spot abstract motifs in the generated images. As @fchollet said, the ability to decompose perceptual information into its abstract constituent blocks is a core feature of human cognition. So Picbreeder was bootstrapping the iterative creation of a highly factorised modular architecture, by leveraging humans' ability to do just this. It's interesting that the Sakana guys referred to serendipity as a "je ne sais quoi". It is paradoxical because it IS the state of understanding abstractly, perhaps without the conscious awareness of it. It's certainly true that when I was breeding the image below, I locked into a particular feature, and it became a convergent, "goal-orientated process". But the important thing is it was completely individual agency. I was following my own path, and it was extremely shallow agency. I wasn't trying to achieve a complex objective. I was merely trying to encourage the materialisation of the abstract feature I initially recognised. When we look at the phylogeny created in this non-interactive way, we see that deep abstractions are not locked in and shared in deep lineages above. I think the two main problems of automating this process with LLMs are: convergence in sampling, and the inability to understand perceptual information in an abstract and systematic way. Both of these problems are directly explained by the fractured entangled representation hypothesis. So if this was an experiment trying to find if we could produce a factorised phylogeny from a non-factorised phylogeny (LLMs), it seems to have proven otherwise at this scale. However, we shouldn't give up so easily, if there is, indeed, even the slightest gradient of factorization, this process could presumably be scaled and repeated. I'm also intrigued by some of the work from Goodfire, which seems to suggest that factorization does meaningfully increase with scale, even on SGD networks (@banburismus_ ). Anyway, in my opinion, this is one of the most important research directions in the whole field of AI: leveraging open-ended methods and building networks with factorised representations. Looking forward to lots more exciting work from Sakana! VLMは人間のような創造性を持てるか？ ケネス・スタンレー教授らの『目標という幻想（Why Greatness Cannot Be Planned）』は、明確な目標を設定することが、かえって真に偉大な発見を遠ざけてしまうという逆説を論じた書籍です。その議論の中核にあったのが「PicBreeder」の実験でした。 PicBreeder では、ユーザーが「面白い」と感じた画像を選び、それを少しずつ進化させていきます。事前に決められたゴールはなく、人々が「なんとなく良い」と思ったものを選び続けるだけで、顔や動物、乗り物、頭蓋骨といった予期しない形が、何世代もか…

Source: David Ha (X) | 2026-07-11