ARCHITECHT Daily: AI is all about your data. And maybe your processor.

If you recall the early days of big data, there was a lot of talk about quantity versus quality. On t
ARCHITECHT Daily: AI is all about your data. And maybe your processor.
By ARCHITECHT • Issue #54
If you recall the early days of big data, there was a lot of talk about quantity versus quality. On the one hand, you had Peter Norvig and Google talking about the unreasonable effectiveness of data—which makes a lot of sense at Google’s scale and with, especially at the time, the limited types of data and scope of things it was trying to analyze. On the other hand, you had pragmatists reiterating the old mantra of “garbage in, garbage out.”
Untold millions of MapReduce jobs later, I think most people can agree that both things are true. It’s true for any sort of data science or predictive analytics process, and it’s especially as more people and organizations start experimenting with artificial intelligence, and deep learning specifically. 
I came across three completely different types of content—a blog post, a research paper, and a Quora answer—this week that help drive this point home. Enjoy:
Best practices for applying deep learning to novel applications: This is a really informative paper, from a U.S. Navy researcher, explaining to novices how to get started with deep learning.
What are the best sources to study machine learning and artificial intelligence?: The top answer from Kaggle co-founder Ben Hamner is great. Among his lessons: “Good problems to start with have several criteria … [including] Data is readily available that’s well-suited to addressing the problem (otherwise the bulk of your time will go here).”
Is your data holding you back? This is a really good overview on data gaps from Silicon Valley Data Science—essentially, the process of figuring out what you have, what it’s good for, and what needs to be done to make it good for thing for which you want it to be good.
Oh, and it turns out that when you’re doing deep learning, having the right processors in place can also make a very big differenceGoogle says it is getting remarkable performance and efficiency improvements from its custom-built Tensor Processing Units. In fact, its TPUs are so efficient—and so widely used, like every time someone does a voice search on their phone—that they’ve saved the company from having to build additional data centers.
If you’re wondering why Intel is investing so many resources into AI, look no further than those TPUs at Google. Beyond wanting to own on-device processing for things like computer vision, I think Intel is also banking on the possibility that GPUs might not be the long-term answer to mainstream AI workloads (even though they are today and, by the way, IBM’s cloud now offers the newest, most-powerful Nvidia GPUs as a service). If Google’s TPUs are 15-30 times faster than GPUs and CPUs, and 30-80 times more efficient, you can bet other cloud providers, web companies, and large enterprises doing AI are going to want that type of performance for themselves.

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The biggest news, best analysis and most insightful interviews in cloud computing, artificial intelligence and software engineering.
Artificial intelligence
Every time we read about this type of research, we should be dismayed at how bad hospitals still seem to be at gathering and utilizing medical records. There is life-saving data in there.  •  Share
It turns out that deep learning models can learn a lot about cellular organization.
… but Cognex buying Vidi actually represents a great, if mundane, use case for computer vision—barcode readers. 
Agriculture is so, so important to the world’s future. It’s always good to see computer vision applied to crop health, and I’m certain the investments in this space are only picking up.
And it released a large audio dataset of musical notes. Not life-changing stuff, but this could eventually change the way music is made.  •  Share
Kudos to Facebook and a good use of AI+human processes, but why are we just starting to crack down on stuff like this now? 
Cloud and infrastructure
What’s the best way to make sure you can capture an up-and-coming market? Build out the infrastructure to support them actually using the cloud. 
This is really smart by the DC/OS, Docker, Kubernetes, etc., communities. It’s not a zero-sum game, why try to make it one?
Interesting that scale seems to be less of a concern (but still a big one), while compliance and migration are becoming bigger concerns. I sense the cloud at play.
Your regular reminder that Windows still matters, especially to some of the organizations with deep pocketbooks.
This headline was perfect. Jenkins is ridiculously popular among companies doing CI/CD, which is a lot of companies. It also looks, or looked, like it crawled out of 2001.
Another of many recent breakthroughs in the lab. Five years, people, that’s the timeline Google set for us.
I want to like this idea, but something about it seems off.  I honestly can’t put my finger on what exactly, but involves risk, reward and groupthink.
All things data
This isn’t really about data, but it’s cool to see so many VCs, incubators, etc., tackling scientific research. And much science today does involve a lot of data processing.
This could be promising research for folks who know their data inside and out, but not always how to best present it. Hopefully, that’s scientists more often than data scientists.
The New York Times graphics editor says 85 percent of readers never click on interactive data visualizations. That doesn’t mean you shouldn’t make them, but it does mean you have to be smart when making them.
Not so much a data story as a financial story, but it’s interesting to see Hortonworks and others grappling with whether to stay in London or, potentially, follow their customers out of town.
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