Deep learning impresses and disappoints
Multiple talks discussed results from deep learning techniques, especially convolutional neural networks, and the effectiveness of the methods varied wildly. Some experiments yielded only 50% classification accuracy, which doesn’t ultimately seem helpful or effective at all. I’m unsure whether other techniques were attempted or considered, but it’s clear that deep learning isn’t the most effective approach for every single problem. It’s a shiny new hammer that makes every problem look like a nail. Libraries like TensorFlow make it more accessible, but there is still a visible gap between those who can implement it and those who can implement it effectively.
Re-inventing the wheel
A few groups demonstrated tools that were developed in-house that already have excellent open source alternatives. I’m not sure whether they were unaware of the existing libraries or just wanted something more finely-tuned for their own purposes, but it seems that a lot of scientific time is spent coming up with solutions for problems that are already solved. Regardless, there were plenty of examples of people who did use open source libraries effectively, so the progress there is something to be proud of.