(1) There's a lot more data now for people to use it with (2) Infrastructure for doing it is cheap and scalable (3) Automation is a key driver of progress, and ML algos are now getting good enough to automate a lot of stuff that humans used to have to do.
I think the online ML class (Stanford?) is helping to pump up interest in the laycrowd.
autonomous helicopters
automated analysis of satellite imagery
search engines
visual search engine (google goggles)
virtual assistant (siri)
speech recognition
document classification (spam detectors)
question answering systems (ibm watson)
ad placement (google adsense)
computer guided surgery
high throughput imaging (chemo/bioinformatics)
product recommendation (amazon/netflix)
acturial science
industrial automation (inspection systems)
intelligent video surveillance
I think I should look into this. I sense that I lack a general understanding of what is really possible with ML. (My current understanding is weighted toward underestimating what is possible with ML).
As you can imagine, this generates tons of data. Our lab did a highthrouput screen of genetic mutants in neurons, and then used software to quantify basic morphology such as neurite length , arborization, and cell death.
Crystallographers will use a similar system to bathe their protein in billions of compounds to find the right combination for crystallizing. Automated cameras will capture images and try to identify which ones have crystallized so the researcher doesn't have to do it by hand.