"Attention Is All You Need" - I've always wondered if the authors of that paper used such a casual and catchy title because they knew it would be groundbreaking and massively cited in the future....
I was there. They had no idea. The purpose and expectation of the transformer architecture was to enable better translation at scale and without recurrence. This in itself would have been a big deal (and they probably expected some citations), but the actual impact of the architecture was a few orders of magnitude greater.
There’s the hierarchical reasoning model https://arxiv.org/abs/2506.21734 but it’s very new and largely untested Though honestly I don’t think new neural network architectures are going to get us over this local maximum, I think the next steps forward involve something that’s 1. Non lossy 2. Readily interpretable
By now, I seriously doubt any "readily interpretable" claims. Nothing about human brain is "readily interpretable", and artificial neural networks - which, unlike brains, can be instrumented and experimented on easily - tend to resist interpretation nonetheless. If there was an easy to reduce ML to "readily interpretable" representations, someone would have done so already. If there were architectures that perform si…
From what I’ve seen neurology is very readily interpretable but it’s hard to get data to interpret. For example the visual cortex V1-V5 areas are very well mapped out but other “deeper” structures are hard to get to and meaningfully measure.
By now, I seriously doubt any "readily interpretable" claims. Nothing about human brain is "readily interpretable", and artificial neural networks - which, unlike brains, can be instrumented and experimented on easily - tend to resist interpretation nonetheless. If there was an easy to reduce ML to "readily interpretable" representations, someone would have done so already. If there were architectures that perform si…
From what I’ve seen neurology is very readily interpretable but it’s hard to get data to interpret. For example the visual cortex V1-V5 areas are very well mapped out but other “deeper” structures are hard to get to and meaningfully measure.
They're interpretable in a similar way to how interpretable CNNs are. Not by a coincidence.
For CNNs, we know very well how the early layers work - edge detectors, curve detectors, etc. This understanding decays further into the model. In the brain, V1/V2 are similarly well studied, but it breaks down deeper into the visual cortex - and the sheer architectural complexity there sure doesn't help.
From what I’ve seen neurology is very readily interpretable but it’s hard to get data to interpret. For example the visual cortex V1-V5 areas are very well mapped out but other “deeper” structures are hard to get to and meaningfully measure.
They're interpretable in a similar way to how interpretable CNNs are. Not by a coincidence. For CNNs, we know very well how the early layers work - edge detectors, curve detectors, etc. This understanding decays further into the model. In the brain, V1/V2 are similarly well studied, but it breaks down deeper into the visual cortex - and the sheer architectural complexity there sure doesn't help.
Well, in terms of architectural complexity you have to wonder what something intelligent is going to look like, it’s probably not going to be very simple, but that doesn’t mean it can’t be readily interpreted. For the brain we can ascribe structure to evolutionary pressure, IMO there isn’t quite as powerful a principle at play with LLMs and transformer architectures and such. Like how does minimizing reconstruction loss help us understand the 50th, 60th layer of a neural network? It becomes very hard to interpret, compared to say the function of the amygdala or hippocampus in the context of evolutionary pressure.
That logic does not hold. Being able to provide an immediate replacement is not a requirement to point out limitations in current technology.
What's the value of "pointing out limitations" if this completely fails to drive any improvements? If any midwit can say "X is deeply flawed" but no one can put together an Y that would beat X, then clearly, pointing out the flaws was never the bottleneck at all.
> What's the value of "pointing out limitations" if this completely fails to drive any improvements?
Ironically, the same could be said about Attention Is All You Need in 2017. It didn’t drive any improvements immediately- actual decent Transformer models took a few years to arrive after that.