I live in Portland, this image from today pretty well sums up what’s going on here.

The entire thing feels manufactured by design to get people talking about Cracker Barrel.

No, you are correct. Hinton began researching ReLUs in 2010 and his students Alex Krizhevsky and Ilya Sutskever used it to train a much deeper network (AlexNet) to win the 2012 ILSVRC. The reason AlexNet was so groundbreaking was because it brought all of the gradient optimization improvements (SGD with momentum as popularized by Schmidhuber, and dropout), better activation functions (ReLU), a deeper network (8 layers), supervised training on very large datasets (necessary to learn good general-purpose convolutional kernels), and GPU acceleration into a single approach.

NNs, and specifically CNNs, won out because they were able to create more expressive and superior image feature representations over the hand-crafted features of competing algorithms. The proof was in the vastly better performance, it was a major jump when the performance on the ILSVRC was becoming saturated. Nobody was making nearly +10% improvements on that challenge back then, it blew everybody out of the water and made NNs and deep learning impossible to ignore.

Edit: to accentuate the point about datasets and GPUs, the original AlexNet developers really struggled to train their model on the GPUs available at the time. The model was too big and they had to split it across two GPUs to make it work. They were some of the first researchers to train large CNNs with GPUs. Without large datasets like the ILSVRC they would not have been able to train good deep hierarchical convolutions, and without better GPUs they wouldn’t have been able to make AlexNet sufficiently large or deep. Training AlexNet on CPU only for ILSVRC was out of the question, it would have taken months of full-tilt, nonstop compute for a single training run. It was more than these two things, as detailed above, but removing those two barriers really allowed CNNs and deep learning to take off. Much of the underlying NN and optimization theory had been around for decades.

Before AlexNet, SVMs were the best algorithms around. LeNet was the only comparable success case for NNs back then, and it was largely seen as exclusively limited to MNIST digits because deep networks were too hard to train. People used HOG+SVM, SIFT, SURF, ORB, older Haar / Viola-Jones features, template matching, random forests, Hough Transforms, sliding windows, deformable parts models… so many techniques that were made obsolete once the first deep networks became viable.

The problem is your schooling was correct at the time, but the march of research progress eventually saw 1) the creation of large, million-scale supervised datasets (ImageNet) and 2) larger / faster GPUs with more on-card memory.

It was fact back in ~2010 that SVMs were superior to NNs in nearly every aspect.

Source: started a PhD on computer vision in 2012

Oregon has been entirely state-wide mail-in voting for decades. Good luck shutting down an entire state’s mail for more than a month.

I hear what you’re saying, but it’s worth noting that Newsom is far from an opponent to the trans community. He is the governor of CA, which is famously a strong trans sanctuary state. He was the major of San Francisco, and supported LGBTQ+ rights to the point that the city was issuing marriage certificates before it was federally recognized.

As for his other recent stances, I think him saying transgender athletes playing in women’s sports being deeply unfair is, unfortunately, a popular and nuanced opinion. I don’t paint with such a broad brush that I think him saying women’s sports should get careful consideration with trans athletes as being the same thing as him saying trans people shouldn’t exist.

“We need to put the Warfighter first inside the DoD…”

Votes to confirm a TV personality as SecDef.

For those who don’t know what this is from, it’s a 2021 Netflix special that came out during COVID lockdown called “Inside” by Bo Burnham.

If you haven’t watched it, go do that immediately. Also, a bit of a warning, it deals with some heavy concepts that were very relevant a year or so into lockdown. I rewatched it recently and had to fight off some PTSD feelings.

Everybody in this thread needs to read Project Hail Mary by Andy Weir.