> This gestalt isn't just for biological organisms, but any system for which its decision making engages with representations of the external environment unified with a self-representation to form a coherent representation of a persistent entity engaged with an external world.
This doesn't seem quite right, or at least underspecified. We can talk about this stuff concretely these days, at least in the context of digital systems. E.g. i can draw up a diagram of a system that takes in some camera and audio data (and tactile, proprioceptive, etc.), tokenizes it then runs that + past state data through some autoregressive VLM to drive an inference process. The state being passed around can be written out analytically for a given trained model - the external and internal environmental representations, the linear algebra that transforms them into latent action representations, the process by which that is transformed into control signals. It seems difficult to claim that the computational process that implements this has any more or less of a gestalt then one multiplying two matrices together. So it's not just the existence of certain representations or computational loops that seems to lead to possessing a gestalt.
The two outputs (an image and a string sequence) are learned from entirely different data, even when they're generated with the same model, which is not always the case. There's nothing intrinsic to the learning process that connects image generation and procedural graphics code.
If one wanted to try that, it would likely take the standard LLM finetuning route - scrape a bunch of data pairs at some expense, write some prompts, and have at it.
Is it inflammatory if it's true? Seems rather embarrassing (but fitting for 2026 America) to shy away from painful truths because they're uncomfortable.
I went through a similar cycle. Going back to simplicity wasn't about laziness for me, it was because i started working across a bunch more systems and didn't want to do my whole custom setup on all of them, especially ephemeral stuff like containers allocated on a cluster for a single job. So rather than using my fancy setup sometimes and fumbling through the defaults at other times, i just got used to operating more efficiently with the defaults.
Pass a law requiring cloud compute providers to accept a maximum user budget and be unable to charge more than that, and see how quickly the big cloud providers figure it out.
Interesting... i wonder what the data is to back up that assertion?
Another interesting tidbit in there was that very few cases (1%) appear to be totally asymptomatic, and that most infections apparently start showing some symptoms after a couple days. So there are a few assertions in total that seem to depart from what was previously understood, at least through mass and social media.
I'm very interested in answers to this question as well. I'm working near this space and have a lot of thoughts on what such a service would need to be successful, at least for image tasks. I think it could offer real value to the work forces that complete data labeling tasks, cutting out middleman companies with worker-owned labeling and marketplace software tools.