Cloudera buys a research lab; Facebook and Microsoft make AI portable; and IBM+MIT

So Cloudera announced its second quarter earnings on Thursday, and they were fine, but the real news
Cloudera buys a research lab; Facebook and Microsoft make AI portable; and IBM+MIT
By ARCHITECHT • Issue #135
So Cloudera announced its second quarter earnings on Thursday, and they were fine, but the real news is that it also announced the acquisition of Fast Forward Labs. The acquisition of a research company would be interesting enough on its own, but this deal is made even more interesting because FFL was founded by Hilary Mason—the former chief scientist at Bitly and a very big name in the world of data science. She’s now Cloudera’s vice president of research.
Mike Olson of Cloudera does a good job explaining FFL and Cloudera’s plans for it in the blog post linked to above:
[Hilary] and her team join us to deepen our expertise in applying machine learning to practical business problems, and to give Cloudera a much clearer view of the future of the field.
Fast Forward Labs’ tag line is “reporting on the recently possible.” This is key to FFL’s value to Cloudera: They continually survey academic and industrial research for new techniques. They take those that are newly available and use them to attack actual business problems, building code and developing expertise in applying those techniques to real-world problems. Their customers, and now ours, benefit early from the latest advances in applied AI.

And so, today, we are launching Cloudera Fast Forward Labs. Hilary and her team, working closely with others here, will continue to investigate and report on the state of the art in machine learning and applied artificial intelligence. We’ll continue to apply those techniques to critical business challenges for large enterprises. We’re adding the research subscription to our portfolio. Each of us has a solid customer base that can benefit from the products we offer together.
While we don’t pre-announce offerings, I expect Cloudera Fast Forward Labs to extend both our product and our services portfolios over time.
I’m very curious to see how this plays out, but the idea seems solid in theory. If Cloudera can adequately fund FFL, it could serve as a smaller-scale, and applied, version of the research labs that drive so much innovation at companies like Google and Microsoft.
FFL was doing targeted research on new technologies, and helping clients figure out how to incorporate them into their businesses. Cloudera builds products in these same spaces. While there’s certainly money to be made selling research and advising, that might pale in comparison the long-term benefits of figuring out what works and what types of products will have broad market appeal.
If the acquisition pays dividends, I’d expect to see research divisions popping up in a lot more companies that compete with Cloudera or want to follow in its footsteps.
From my POV, the other big announcement on Thursday is that Facebook and Microsoft released the Open Neural Network Exchange standard for porting AI models from one framework to another. This has the potential to be pretty big because, as Facebook’s Joaquin Candela explains in the blog post, different teams often use different frameworks. And it would be great if that work was more transferable between, say, research and production:
When developing learning models, engineers and researchers have many AI frameworks to choose from. At the outset of a project, developers have to choose features and commit to a framework. Many times, the features chosen when experimenting during research and development are different than the features desired for shipping to production. Many organizations are left without a good way to bridge the gap between these operating modes and have resorted to a range of creative workarounds to cope, such as requiring researchers work in the production system or translating models by hand.
We developed ONNX together with Microsoft to bridge this gap and to empower AI developers to choose the framework that fits the current stage of their project and easily switch between frameworks as the project evolves.
Of course, there’s also a business angle here, which would help explain Microsoft’s participation. ONNX currently supports PyTorch, Caffe2 and Microsoft’s Cognitive Toolkit, but noticeably does not yet support Google’s TensorFlow or Amazon favorite MXnet. It’ll be a different story if those companies get involved and brings their frameworks into the fold, but for now Microsoft—which is doubling down on open source on Azure—gets to look like the company enabling open AI, too.
Finally, there was IBM and MIT announcing the new MIT-IBM Watson AI Lab, which IBM is promising to fund with $240 million over 10 years. Given all the flak IBM has taken over Watson during the past few years—and, really, over the past few months—partnering with MIT could be a very smart idea. Some fresh blood might be good for IBM in terms of thinking outside the Watson box, and the lab’s focus on creating companies to commercialize its work could help give IBM a strategic M&A advantage. 
Partnerships like this have worked out pretty well at UC-Berkeley over the years (e.g., with AMPLab and now RISELab), but those don’t have the tight connection with one specific company. If this works, IBM could come out looking really good. If not, well, IBM’s growing list of critics won’t be surprised.

Sponsor: Bonsai
Sponsor: Bonsai
Artificial intelligence
I don’t know if the plan here is to improve the Spanish-speaking capabilities of its Echo devices and cloud APIs, but that would certainly be a good idea. 
The pilot program will be in the Bay Area, but it sounds like this could expand into other cities, as well. The focus on user experience is a nice angle, because there’s a lot more to hailing a taxi or ordering a Lyft than the actual driving.
This story is about how a company called Avitas Systems is working with Nvidia, including using its DGX-1 “supercomputer” on the backend to train AI models, paired with on-site systems for local processing. And if you want the world’s geekiest unboxing picture, check out this team from the MGH & BWH Center for Clinical Data Science opening the first Volta-based DGX-1 system.
This is good. The business world needs more down-to-earth explanations how it can actually embrace and use AI with its existing data and existing systems. 
His ideas about how robots could use household maps are pretty good, and probably not too worrisome to most people. The idea of selling that data is what got people’s hackles up.
Who knows what will come of Intuitive, but it’s remarkable how far chip research has spread and how many companies are working on them for AI alone.
Rodney Brooks, who helped create MIT’s CSAIL, is now required for me reading on all things AI. A dose of reality is often desperately needed when the hype machine gets fired up.
A good paper from Volkswagen researchers on how and where AI and machine learning are currently being applied by auto companies.  •  Share
A group of Harvard researchers propose a distributed deep learning architecture that takes advantage of local, edge and cloud computing, and aims to maximize fault tolerance and privacy.  •  Share
Sponsor: DigitalOcean
Sponsor: DigitalOcean
Cloud and infrastructure
This was just a matter of time. I look forward to the inevitable price comparisons against Amazon Direct Connect.
Another interesting post from Metamarkets on its infrastructure evolution, this one detailing how it’s using both AWS Direct Connect and Google Interconnect. But the real story here might be Synoptek, which handles the hardware part the equation so a cloud-only company like Metamarkets can go multi-cloud without buying and managing gear.
Datadog is already pretty popular, and expanded from systems monitoring to APM earlier this year. Now it is adding log analysis to its mix. I spoke with Datadog CEO Olivier Pomel on the podcast back in March.
This is curious. On the one hand, Atlassian has good penetration already with other tools, making an upsell to include Stride that much easier. On the other hand, Slack.
Obviously, IBM is pretty sure AI will be a saving grace, too. We also can’t forget about quantum computing. Basically, IBM has its finger on the pulse of three big technologies. Now it just needs to capitalize.
Krikorian, who was an engineering exec at Uber and Twitter before this role, is a smart dude. It sounds like he might also bring the Democratic party into the 21st century, too.
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All things data
I honestly haven’t heard much about this company before, but $240 million is a lot of capital for a Series B. I’ll assume they’re onto something.
This is both a nicely detailed history of search architectures and problems at Reddit, and also more proof that even tech-savvy companies really will pay for software if it’s good enough and their need is great enough.
I wonder how Microsoft would market a tool like this, given the existence of established technologies like Trifacta. My guess would be a fairly tight integration with its broader suite of cloud services and data science tools.
New ARCHITECHT Show every Thursday; new AI & Robot Show every Friday!
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