Kick Off:
“Overcomplicated.” Do you ever feel that way about our increasingly tech-driven world? That’s the name of a new book I read this week by a complexity scientist named Samuel Arbesman. It’s a good read, accessible to anyone. Beyond giving good explanations of basic programming concepts like recursion (when code refers to itself), it walks through scores of past examples of when models broke down and man couldn’t understand why.
The reason many of our systems have become too complicated for any human to understand is that their creators fidget with them until they work but don’t quite understand why it worked in the end. I can’t think of a better example of this than the new algorithms underpinning the AI resurgence: deep learning. These new systems are grown and trained – they aren’t designed. So they can have really unexpected behaviors outside of the ones they are intended for. I don’t think we should be overly alarmist about all this, but it’s important to understand this as we plow forward with advancements in this area.
In the News:
Workday acquired the big data company Platfora, which built a data analytics platform on top of Hadoop. These sorts of acquisitions show that an entire mini-economy has been built around Hadoop, an open-source storage and processing platform for large data sets. I would caution people from getting more wrapped up in Hadoop because of more advanced open-source systems, like Spark, that have come along. This tends to happen with industry and computing — as soon as industry figures out a big change and adapts, the developers are onto something much better.
There is so much data out there now that researchers are constantly trying to combine data sets to look for causal effects. But that can’t always accurately be done. Some computer scientists at UCLA and Purdue have developed a mathematical tool called a structural causal model, which figures out how information from one source can be combined with data from another source. As they explained, it’s “like putting together a jigsaw puzzle using pieces that were produced by different manufacturers.” Technical write up here. Layman write up here. Cool stuff.
More new hardware out from Nvidia. The chip maker continues to make investments in new GPUs. It’s well known that that GPUs, or graphics processors, have been a primary catalyst of the resurgence in AI. But I was surprised to hear that Nvidia announced this GPU at an AI meetup, and the new chip has specific instructions designed for deep learning. It’s a sign that AI may come to dominate graphics processing units’ future more than its early driver, video gaming. At what point do we start calling it an AIPU?
Autopilot saved a pedestrian life in DC this week. People can worry all they want about self-driving cars, but there is technology coming out of the push to self-driving cars that will benefit society, even if we keep drivers behind the wheel.
In Industry:
It is super prestigious to become a doctor, but apparently many doctors now spend several hours a day entering data about their patients. Interesting how bringing technology into a field can reshape jobs and may sometimes mean that new data-clerk jobs need to be created.
So Spotify is now offering advertisers targeted ads, using the data about your listening patterns. But I’m not sure how someone buying a 15-second audio ad spot would figure out whether someone who listens to smooth jazz is more desirable than someone who, say, listens to heavy metal.
Data centers are known to be big energy wasters. So it’s cool to see Google testing AI systems to improve the energy efficiency of its data centers. There’s something very meta here: the data centers are where AI gets trained and now AI can help them be more energy efficient, all the while creating more data to store.
Quirky Corner:
Not sure if any of you are playing Pokemon, but beyond its goofy fun is some seriously advanced use of mapping technology. Here’s a good Bloomberg story on the start-up behind that.
Love this video of Facebook’s solar powered, Internet-beaming plane.
Last, if you’re spending time at the beach this summer and you want to know what’s across the ocean from you, take a look at these maps. It’s not always what you think.
Braxton McKee is the technical lead and founder of Ufora, a software company that has built an adaptively distributed, implicitly parallel runtime. Before founding Ufora with backing from Two Sigma Ventures and others, Braxton led the ten-person MBS/ABS Credit Modeling team at Ellington Management Group, a multi-billion dollar mortgage hedge fund. He holds a BS (Mathematics), MS (Mathematics), and M.B.A. from Yale University.
