In 2014, "Predictive Analytics" hit the mainstream. Many people got very excited about the idea that you could take a pinch of "big data" or "data mining", add in a dash of "visualization", and get "business value". I agree. I only use the air quotes because it was framed as something novel. But this stuff has been going on for decades, (though to be fair for much of that time it was with smaller datasets). For example, go to the appendix of Friedman's famous 1991 MARS paper and you'll find data mining + visualization for new insights. And then there's statistics + Tufte-style visualization. Then you have the likes of Spotify and Tableau. We'd been doing this sort of thing at Solido since 2004, and ADA before that, to help designers get insight into designing computer chips. My PhD included "knowledge extraction." It's great to see that this tech is starting to hit the mainstream - it's incredibly useful.
What's cool is that there is state of the art beyond predictive analytics. It's basically about closing the loop, rather than working with a static dataset. Get some data, do some analysis, but then (auto) find new data and repeat. The "find new data" part can be active, i.e. you can choose which sample to take next. You could also think of it as classic optimization, but with a visual element. I call it "Active Predictive Analytics", or "Active Analytics" for short. We've been doing this with a new tool at Solido, and designers really like it as a new style of design tool. It turns out to address auto vs. manual design too..
There's been a long running debate on whether automatic or manual design is better, and both sides have had really great arguments. But what if you can get the best of both worlds, if you can reconcile manual vs. automatic design? That's what the tool turns out to do: if you want to design fully manually, i.e. you pull the design, you can. If you want fully automatic, i.e. the tool pushes the design, you can. But the cool thing is that it allows the shades of gray in between: it gives insight what designs and design regions might be good, and you can easily pull the design with a visual editor. Call it supercharged manual design, if you will. I'm quite excited about this because it has applications far beyond circuits, for everything from deep learning to business intelligence to website optimization (evolution from A/B testing to multi-armed bandit to this).
I gave an invited talk on this at the Berlin Machine Learning group in May 2014. Slides are here.