Of the people who are still alive, Hopfield and Hinton make sense.
Hopfield networks led to Boltzmann machines. Deep learning started with showing that deep neural networks were viable in Hinton's 2006 Science paper, where he showed that by pre-training with a Restricted Boltzmann machine (essentially a stacked self-supervised auto-encoder) as a form of weight initialization, it was possible to effectively train neural networks with more than 2 layers. Prior to that finding, people found it was very hard to get backprop to work with more than 2 layers due to the activation functions people were using and problematic weight initialization procedures.
So long story short, while neither of them are in widespread use today, they led to demonstrating that neural networks were a viable technology and provided the FIRST strategy for successfully training deep neural networks. A few years later, people figured out ways to do this without the self-supervised pre-training phase by using activation functions with better gradient flow properties (ReLUs), better weight initialization procedures, and training on large datasets using GPUs. So without the proof of concept enabled by Restricted Boltzmann Machines, deep learning may not have become a thing, since prior to that almost all of the AI community (which was quite small) was opposed to neural networks except for a handful of evangelists (Geoff Hinton, Yoshua Bengio, Yann LeCun, Terry Sejnowski, Gary Cottrell, and a handful of other folks).
Hopfield networks led to Boltzmann machines. Deep learning started with showing that deep neural networks were viable in Hinton's 2006 Science paper, where he showed that by pre-training with a Restricted Boltzmann machine (essentially a stacked self-supervised auto-encoder) as a form of weight initialization, it was possible to effectively train neural networks with more than 2 layers. Prior to that finding, people found it was very hard to get backprop to work with more than 2 layers due to the activation functions people were using and problematic weight initialization procedures.
So long story short, while neither of them are in widespread use today, they led to demonstrating that neural networks were a viable technology and provided the FIRST strategy for successfully training deep neural networks. A few years later, people figured out ways to do this without the self-supervised pre-training phase by using activation functions with better gradient flow properties (ReLUs), better weight initialization procedures, and training on large datasets using GPUs. So without the proof of concept enabled by Restricted Boltzmann Machines, deep learning may not have become a thing, since prior to that almost all of the AI community (which was quite small) was opposed to neural networks except for a handful of evangelists (Geoff Hinton, Yoshua Bengio, Yann LeCun, Terry Sejnowski, Gary Cottrell, and a handful of other folks).