The absolutely insane amount of compute that transformers consume could probably be better used for neuroevolutionary search.
I am fairly certain that if you try, you can show that for any particular sequence of tokens of length N, the N-1 tokens induce a residual FFNN that results in exactly the same distribution over the next tokens given just the Nth.
- I had the whole idea and held someone's hand the whole time they did the grunt work, then I wrote the whole damn paper except for making the fonts correct.
to:
- I am the boss. I am vaguely aware that someone is researching something.
Almost certainly not, but I wonder how well it does at that?
Come up with a (fake) way to improve the transformer architecture, then write the title and abstract.
Title: Novel Cyclical Attention Mechanism Enhances Transformer Architectures
Abstract: The transformer architecture has emerged as a powerful model for handling sequential data across various domains. Despite its success, the fixed nature of its attention mechanism often restricts its ability to adapt to the dynamic nature of real-world data sequences. In this paper, we propose a novel Cyclical Attention Mechanism (CAM) that augments the standard transformer architecture. Unlike conventional attention mechanisms which allocate attention statically based on previous layers, the CAM operates in a cyclical fashion, allowing for a dynamic, recurrent redistribution of attention over the sequence at each layer of the transformer. This cyclical process is facilitated through a novel temporal feedback loop that integrates information from both previous and subsequent layers, allowing for a more nuanced understanding of long-term dependencies within the data. Moreover, the proposed mechanism introduces an adaptive temporal gating system that intelligently modulates the flow of information through the cycles, ensuring optimal retention and refinement of relevant information throughout the network. We demonstrate through extensive experiments on various benchmark datasets that the Cyclical Attention Mechanism significantly improves the model's ability to handle long-range dependencies, leading to substantial improvements in performance across multiple tasks including language modeling, translation, and sequence labeling. Our findings pave the way for a new line of research into dynamic attention mechanisms within transformer architectures, showcasing the potential for enhanced performance and adaptability in handling complex sequential data[1].
I know, I find it tiresome too when people share their ChatGPT responses, but this really struck me. We are very, very close to those being indistinguishable.
* I'd hate to be trying to sort out valid from invalid papers these days.
* How close are AIs to doing AI research?
* If an AI can predict something similar to your paper, is it more or less likely to be valid/true/reproduceable?
[1]: https://chat.openai.com/share/ba769733-e98d-48d3-809a-7611f3...
like all the Saas feature pages.
You should give the Fast Weight Programmers paper a chance, and a thorough reading. It sounds like you already appreciate a fair bit of its main point.
The best part about the FWP paper is the derivation of an FWP equation from the transformer equation. It's remarkably straightforward. You remove the softmax operation (i.e. linearize) and the rest is just algebraic manipulation -- a formal proof.
Transformers are just NNs that learn to control a Content-Addressable Memory (CAM).
This perspective has far-reaching implications for ML, sort of like category theory did for metamathematics and type theory. For example, LSTM cells can be viewed as a NN that learns to control a flip-flop (the "deluxe" kind found on FPGAs, with output-enable, clock-enable, and reset inputs). I've found that this is by far the easiest way to explain LSTM to people. It also raises the obvious question of what other kinds of simple blocks can be controlled by NNs. I think this question will lead to another wave of breakthroughs.
The ultimate limit of this approach is the Gödel Machine -- although no attempt to build one has come anywhere close to success yet.