Ignoring future Google research in ethics has low-risks, as top ethics talent will certainly avoid working for Google, and prefer academic freedom elsewhere (where reviewers are external, to avoid CoI).
French President Macron seems to be doing what this post is writing: trying to fine-tune the spread, and find a balance between economy and health, with proportional measures
posts from health professionals can be even worse. this one was not too bad at explaining the thinking of ruling elites (who think they can fine-tune an epidemic)
Because researchers have little incentives to commercialize their research, especially in academia. They take pride of this insulation from the 'real world'. That's the root cause why research metrics are different from industry metrics.
This professor gave an argument in his paper. I gave a refutation. If you agree with him that char-CNN was a sophisticated model in early 2017, then you are not well-informed about the situation in deep learning.
The MoleculeNet co-author gave another argument in the comments. My refutation is that you can't claim to lack time after 8*20 man-months have passed.
I am not sure to fully understand your remark, but if you have a benchmark graph convolutions vs. char-CNN, it would be great to write your result and post it on Arxiv. Pande will be interested ;) The problem is not with the theoretical motivation, but with the empirical confirmation.
I also agree that there are many ways to work through the perfect discriminator problem for ORGAN. But it remains to be done (afaik).
From a user viewpoint, Deepchem would greatly benefit from being a better team player with lower-level (Tensorflow) or other (Pytorch) frameworks. The pace of research (in NLP in particular) is too fast to make it realistic to port everything in Deepchem without an unreasonable delay.
Does it fit the Deepchem agenda? That's another question ;)
In general, you are right. But in this particular case, char-CNN is the standard used by many people, Stanford included. It is not sophisticated at all.
For me, this omission is a negligence. It is selective laziness.
The author (me) does not have 20 PhD students, postdocs and startuppers under his hand, to do the job for Stanford. With my limited resources, it is more cost-effective to do my job against Stanford ;)
1. I agree that graph convolutions make a lot of sense. However, it is not shown yet that they are better than SMILES, although it might just be a matter of time. A lot of graph variants are possible, or maybe we should look at molecules not as graphs, but as quantum objects (See the other Stanford paper on atom convolutions).
2. Yes, Deepchem is built on top of Tensorflow. This additional layer looks useful, but is it really the case? Using standard NLP models is simpler than using deepchem models, and NLP models might still be the state-of-the-art for chemistry tasks, via SMILES (until we get a reasonably comprehensive benchmark, which MoleculeNet is not).
I am not buying into a chemistry-specific library, until it is shown to be really necessary.