Yes, there’s already work on this:

https://arxiv.org/abs/2010.00770

Aren't people working on ways to implement NN in hardware?

The Gradient Podcast

Gradient Dissent Podcast

Lex Fridman's podcast has also had all the biggest names in AI as guests.

These guys got there first:

https://twitter.com/peterjansen_ai/status/1580686608566583296

https://cognitiveai.org/wp-content/uploads/2022/10/wang2022-behavior-cloned-transformers-are-neurosymbolic-reasoners-arxiv.pdf

https://twitter.com/peterjansen_ai/status/1580686608566583296

Not if it's actually impossible.

I would even say that neural networks are not the be-all end-all of machine learning.

Previously from Deep Mind in the domain of symbolic reasoning:

Making Sense Of Sensory Input

>This paper attempts to answer a central question in unsupervised learning:what does it mean to "make sense" of a sensory sequence? In our formalization,making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the causal theory -- objects,properties, and laws -- must be integrated into a coherent whole. On our account, making sense of sensory input is a type of program synthesis, but it is unsupervised program synthesis.
>
>Our second contribution is a computer implementation, the Apperception Engine, that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the unity conditions.A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and impute (fill in the blanks of) missing sensory readings, in any combination.
>
>We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems,occlusion tasks, and sequence induction intelligence tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data.
>
>The engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction intelligence tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence.

Edit: Also check out the followup paper:

Making Sense Of Raw Input

No, the ancient Roman orator and statesman Marcus Tullius Cicero.

Thanks. Will have a look.

Thanks!

Is there a website that keeps track of all the models being released by the major AI labs?

I guess this sub has them all, but looking for a neater presentation.