This is the correct answer. A key reason Rebco is so much better than older alternatives is that it can be cooled to superconductivity using only liquid nitrogen (77K), as opposed to liquid helium, which is much harder to work with.
Longtime DVC user here - this is going to be so helpful. We use DVC for all of our model and data versioning, but what's been missing is the ability to cleanly integrate that into our CI workflow. Looks like that's solved now! The cml.yaml syntax also looks quite nice, very easy to follow. Looking forward to trying this out.
Great anecdote from Jeff Dean's twitter [1] about how they thought to try this experiment, given that doctors didn't know such a thing was possible:
"Funny story. We had a new team member joining, and @lhpeng suggested an orientation project for them of "Why don't you just try predicting age and gender from the images?" to get them familiar with our software setup, thinking age might be accurate within a couple of decades,and gender would be no better than chance. They went away and worked on this and came back with results that were much more accurate than expected, leading to more investigation about what else could be predicted."
This is not "AI that builds AI". The actual research behind AutoML is called NASNet (https://arxiv.org/pdf/1707.07012.pdf), and all it is simply: we found two good neural network layers (called NASNet normal cells / reduction cells in the paper) that work well on many different image datasets. It's a very cool research result. But it's not something that will replace AI researchers.
The AI Grant fellowship has been an awesome experience so far; I highly recommend applying if you're on the fence. The grant itself is useful (and this round includes $20k of Google cloud credits!) but equally valuable is the community that Nat has curated around the project. There are a lot of great folks working on AI from all sorts of places just a Slack message away. Now that Daniel Gross (YC's AI-focused partner) is involved as well, I'd expect that community to continue to grow.
This post doesn't even mention the easiest way to use deep learning without a lot of data: download a pretrained model and fine-tune the last few layers on your small dataset. In many domains (like image classification, the task in this blog post) fine-tuning works extremely well, because the pretrained model has learned generic features in the early layers that are useful for many datasets, not just the one trained on.
Even the best skin cancer classifier [1] was pretrained on ImageNet.