Thanks for your reply.

>>> I think it would help if you link "NeSy computation engine". I'm actually not familiar with this (not in the symbolic world, but interested. Just never had time, so if you got links here I'd personally appreciate it). I can find the workshop but not the engine.

I linked to it in my previous comment. I'm referring to the ``NeSy computation engine'' described here. I didn't know there was a ``NeSy'' workshop and this paper was my first encounter with the term.

I think it's interesting that you mention symbolic world like it is separate from some other world. There's the AI that was and the AI that is today. There's the AI over there and the AI over here. Whenever you hear someone mention symbolic in the context of AI go ahead and grab a chair because immediately after this they are going to talk about cyc and John McCarthy for at least 20 min. If you're lucky they might throw some Prolog in there.

I don't think this is productive and I don't think there is another symbolic world. There is just the world. There are certain things in the world for which a numeric, directional representation makes sense. There are other things for which it makes no sense at all. It's my view that primitives in language are one of these things. Additionally, there are certain places where it makes sense to consider these representational approaches and other places where it only makes political sense. Lastly, there are symbols - atomic primitives - and there are ``symbols,'' objects with vectors in them and who knows what else.

What's striking to me about this paper is the coverage of formal grammars and semantic parsing entirely within the context of domain adaptation. Definitely the best part is the coverage of compositionality (https://ncatlab.org/nlab/show/compositionality) in the context of composing computational graphs. This is striking to me because all of these things (except domain adaptation) are essential to any reasonable theory of meaning but they are covered as if they've been repurposed for the practical application of populating Google Knowledge Panels, which I believe is exactly what happened. Check out the definitions of semantic parser and symbol.

>> Domain adaptation across verticals >>> This is also a bit vague and so I'm not sure what you _specifically_ mean.

Crude semantic attributes pulled from character sequences and mapped onto the latent space of images have utility in business contexts if the mapping for some term sufficiently distinguishes it from the mapping of another term that has the same surface form. It ends there. GloVe was a half-baked representation of meaning in language when it was adapted from word2vec in 2014. GPT-2 grabbed the torch in 2019. It still doesn't work. Well, it sometimes works for adapting a general model to a specific domain such as a business vertical, but only in a crude and superficial way. Note that almost no ML research today discusses this representational issue at all, and that almost all ML research takes this representation as a starting point. If you decide to publish hyperparameters in your paper, such as in an appendix, hyperparameters related to vocab size and the dimensionality of your embedding space often aren't even worth mentioning. That's fine, I guess, because they don't mean anything anyway, but not talking about this, in my view, is not fine.

Check out the Mamba paper for example. Like most of ML research today the focus is on optimization. The representation problem has been solved so there's no need to talk about it: we map everything onto the latent space of images because short-form video content rules the day and that's how dude is gonna hit his 7T: advertising ([link redacted]).

>>> I think the problem is that math is t...