Schmidhuber's disappointment should be with IDSIA or others in his network for not nominating him. The ACM does not itself survey the field looking for worthy candidates - the process is entirely driven by nominations which need to be supported by endorsements, solicited by the nominator, from heavyweights in the field. The maximum size group an award can be given to is three.

The nomination process is private, so it's not publicly known who nominated Bengio/Hinton/LeCun, but given the common CIFAR connection it might be a reasonable guess that someone there might have organized the nomination, maybe self-initiated with the goal of it reflecting well on the organization, or perhaps lobbied for by the recipients.

No doubt some aspects of the Transformer architecture are fungible, but as Hochreiter is implicitly proving you can't just scale up an LSTM and get Transformer level performance out of it, which is why he has come up with this new xLSTM architecture to try to do better!

The short 2K Transformer context size that Hochreiter is using for xLSTM comparisons seems a bit suspect ... Of course the attraction of an RNN is that it has "infinite" context/memory, so it may be expected to outperform a short context Transformer, while at the same time context scalability is an issue for RNNs, even an LSTM. Has he just cherry picked the size at which the advantages of an xLSTM outweigh the disadvantages ?

Note that despite the table saying GPT-3, he isn't actually testing against GPT-3 (a 175B model), but rather a 400M GPT closer to GPT-1 in size. The only reason he's calling it "GPT-3" is because of the 2K context size.

Could a 1T param xLSTM one-shot a compiler or find a needle in a 1M token haystack? Does an induction-head-like AB => A'B' in-context learning primitive, or something functionally equivalent, emerge out of stacked xLSTM layers?

At the end of the day it's prediction power that matters, not specific architecture, but we've yet to see any other architecture that functionally competes with a large Transformer. It would be neat to see a significantly different one that did!

That briefly boosted the demand for GPUs (until much of it switched to ASICs), but the existence of GPU compute in the first place came from gaming and NVIDIA's choosing to implement programmability the way they did.

The existence of cheap parallel processing was certainly a necessary enabler for large neural networks to take off.

I don't know who else may have experimented with using GPUs for neural nets before 2012 (not so easy since it required hand coding everything in raw CUDA - no framework support), but AlexNet drew a lot of attention (it was a very dramatic win of the ImageNet 2012 competition - the first neural net entrant, and a win by a very large margin), and large neural net research just accelerated from there.

The PDP project was very early - relevant in term of neural net history of course, but hard to see much there relevant to today's large models other than Hinton's reinvention of SGD as an alternative to the layer-wise training that was then the norm.

One interesting thing to note from the PDP handbook are mentions by LeCun and Hinton of what would later be called CNNs, which LeCun claims to have invented. It seems that Hinton deserves just as much credit as LeCun, and in any case these are discussed just as locally connected models using shared weights as an optimization.