Academic example but I'm sure it's made it's way to this industry as well: in bioinformatics, this has proven tremendously useful for approximate string matching to do things like identify microbes in sequenced microbiome samples - cf https://genomebiology.biomedcentral.com/articles/10.1186/s13...
I wonder how driverless cars will effect this. Similarly I'm wondering if ride-sharing apps already are. Single cars picking up 4 adults that would otherwise be driving could potentially hugely reduce the number of cars on the road, making commutes shorter for all involved.
Long reads from PacBio, Oxford Nanopore, and 10x are also exciting. This new tech coupled with HiC for scaffolding and single cell sequecing brings up the possibility of complete knowledge of your genome, the collection of strains in a metagenome, or all the types of cells in a tissue/cancer sample.
The main one is vg, which I think Benedict Paten is actively supervising, along with Richard Durbin. The main developer is Erik Garrison. I'm pretty surprised it wasn't mentioned by name: https://github.com/vgteam/vg
Actually there are new approaches to study exactly that, and they are developing rapidly, cf: https://en.wikipedia.org/wiki/Chromosome_conformation_captur...
Basically, this problem, along with a long list of other applications is being attacked by deep sequencing. The way it works is that you apply a treatment to DNA that 'glues' 3D contacts in place and covers the glued segment, apply restriction enzymes to cut out what's not covered by glue, get rid of the glue, sequence all that's left, and then map the remaining sequenced fragments to the reference genome. The output is the relative tendency of different regions to come into contact with each other.
The mention of groups of robots learning together by exposing themselves to the real world and sharing lessons is very interesting to consider. I wonder if this is inspired by ants searching for food and leaving scent trails. Are there success stories of this approach in robotics already?
I don't think their intent is to replace existing database servers. My impression is that they have copied the data onto their servers to make analysis on their cloud service more attractive. For example, the fact that the 1000 Genomes data is there saves researchers from having to go to the trouble of uploading it themselves.
Outside of DBs, indexes have shown themselves to be extremely useful in string problems in bioinformatics (my area of research). The modern workhorses of this trend are the Burrows Wheeler transform + FM index (together) and Bloom filters (also mentioned in the link). These have been applied to sequence alignment, de novo assembly of genomes, and compression of sequences. I posit the same bag of tricks can be applied in the NLP/machine learning settings, but I know less about how commonly they have been.
Twitter is actually becoming quite popular in science. It's a great tool to hear of new papers, find out about new findings reported in conferences you can't attend, and communicate in a public forum. Don't think you would have had much of that in MySpace...