> I happen to believe that the future of AI requires a specialized notation. I can't put my finger on what this means at this time. This might be a worthwhile p

> I happen to believe that the future of AI requires a specialized notation. I can't put my finger on what this means at this time. This might be a worthwhile pursuit at a future time, if I ever retire (I can't even think about what that means...I love what I do). I also share this opinion and I might know what you mean. A lot of breakthroughs in physics are due to new notation – e.g. Maxwell's equations, Einstein notation, etc. Or to be precise, it is easier to think new thought in notation/language that is suited for it. Current machine learning is a 90:10 blend of empirical stuff followed by delayed theory. A language for theory-based ML is math with paper and pencil. However language for empirical experiments are PyTorch, Tensorflow, JAX, DEX, Julia, Swift, R, etc. Former is "dead" historical language, latter are notations for differentiable computational graphs that can be run on modern HW accelerators. If you look at what have those programming languages in common is that they were all influenced by APL. And funny enough, machine learning is the best use case for APL, but practically non-existent. APL should be reborn as differentiable tensor-oriented notation. That would be wild – prototyping new ML architectures at the speed of thought. Anyway, another angle for APL + ML is an alternative interface – write APL with pencil on paper that understands your handwriting and evaluates your APL scribbles. [0] I committed myself to provide a BQN [1] with such interface and see where it leads. Ideally, the best outcome would be the combination of both approaches. [0] https://www.youtube.com/watch?v=0W7pPww6Z-4 https://www.youtube.com/watch?v=0W7pPww6Z-4 [1] https://mlochbaum.github.io/BQN/ https://mlochbaum.github.io/BQN/