I read your post and it does not describe a way for us to “directly design desirable features” in our current ML paradigm. I think “current ML is very opaque” is a very accurate summary of our understanding of how current ML systems perform complicated cognitive tasks. (We’ve gotten about as far as figuring out how a toy network performs modular addition.)
How familiar are you with loras, textual inversion, latent space translations and the like? Because these are all techniques invented within the last year that allow us to directly add (or subtract) features from neural networks in a way that is very easy and natural for humans to work with. Want to teach your AI what “modern Disney style” animation looks like? Sounds like a horribly abstract and complicated concept, but we can now explain to an AI what it means in a process that takes <1hr, a few megabytes of storage, and an can be reused across a wide variety of neural networks. This paper in particular is fantastic because it allows you to define “beauty” in terms of “I don’t know what it is, but I know it when I see it” and turn it into a concrete representation.
That does indeed seem like some progress, though note that it does not really let us answer questions like “what algorithm is this NN performing that lets it do whatever it’s doing”, to a degree of understanding sufficient to implement that algorithm directly (or even a simpler, approximated version, which is still meaningfully better than what the previous state-of-the-art was, if restricted to “hand-written code” rather than an ML model).
I think that to the extent we need to answer “what algorithm?” style questions, we will do it with techniques like this one where we just have the AI write code.
But I don’t think “what algorithm?” is a meaningful question to ask regarding “Modern Disney Style”, the question is too abstract to have a clean-cut definition in terms of human-readable code. It’s sufficient that we can define and use it given a handful of exemplars in a way that intuitively agrees with humans perception of what those words should mean.
How familiar are you with loras, textual inversion, latent space translations and the like? Because these are all techniques invented within the last year that allow us to directly add (or subtract) features from neural networks in a way that is very easy and natural for humans to work with. Want to teach your AI what “modern Disney style” animation looks like? Sounds like a horribly abstract and complicated concept, but we can now explain to an AI what it means in a process that takes <1hr, a few megabytes of storage, and an can be reused across a wide variety of neural networks. This paper in particular is fantastic because it allows you to define “beauty” in terms of “I don’t know what it is, but I know it when I see it” and turn it into a concrete representation.
That does indeed seem like some progress, though note that it does not really let us answer questions like “what algorithm is this NN performing that lets it do whatever it’s doing”, to a degree of understanding sufficient to implement that algorithm directly (or even a simpler, approximated version, which is still meaningfully better than what the previous state-of-the-art was, if restricted to “hand-written code” rather than an ML model).
I think that to the extent we need to answer “what algorithm?” style questions, we will do it with techniques like this one where we just have the AI write code.
But I don’t think “what algorithm?” is a meaningful question to ask regarding “Modern Disney Style”, the question is too abstract to have a clean-cut definition in terms of human-readable code. It’s sufficient that we can define and use it given a handful of exemplars in a way that intuitively agrees with humans perception of what those words should mean.