James Kotecki, our Director of Marketing and Communication, recently spoke at the Strata Data Conference in New York City. From the description:
Miscommunication between business leaders and technical experts can doom even the best data science project. Don’t let it drive you insane! In this session, we’ll dissect many flavors of communication failure, from goal misalignment to machine learning misunderstanding. Then, we’ll explore practical ways to bridge these gaps.
If you missed ODSC West in 2018, then you missed our CEO Robbie Allen give his talk on deploying machine learning in the enterprise. But fear not! The good folks at ODSC have provided video of his presentation.
In this talk, Robbie covers the importance (and definition) of good data and offers guidance for evaluating machine learning opportunities. Take a look!
If you’re interested in seeing our CEO or other team members live and in person, check out our upcoming events.
CEO Robbie Allen recently spoke about the business impact of machine learning at Adobe’s Think Tank on The Future of AI in the Enterprise.
“I really focus on three areas where I think that machine learning specifically will have an impact,” he said.
“One is reducing costs, and by that I don’t mean that we’re displacing the workforce. What I mean by that is that there’s lot of jobs out there that were never intended for people to do. But the only option was for people to do them, because we didn’t have software that could it on their behalf. So I think there will be a lot of those types of jobs – repetitive – it’s not ideally suited for humans to do in the first place.”
“Second is increasing efficiency, and oftentimes you’ll hear about potentially given people superpowers in their job, where not only can they do their job, they can do it a lot faster, a lot better, and they can be much more productive.”
“And then the third are is achieving new breakthroughs, and this is something I think is going to be really powerful as it relates to what you can get out of machine learning. As a society we have blindspots that we’re not aware of. For any new breakthrough that occurs, that only happened because someone else didn’t think to do that. Machine learning gives us an opportunity to discover new patterns, new ways of working that weren’t obvious before, and so I think we’re going to be able to see all sorts of interesting new capabilities in the enterprise as it relates to that.”
During the talk, Robbie was clear about the need to focus squarely on solving real business problems:
“So I think especiallywhen it comes to the enterprise, it’s less about ‘can we properly define AI,’ and really what problems are we trying to solve,” he said.
“And it goes back to, what are the key differences now between what we can do now versus some of the vendors that we’ve seen before. And it’s the computing power, it’s the availability of data, and it’s the advanced algorithms that have been created.”
“And I think really focusing on what problems can you solve with that, that’s really where it’s at for the enterprise, much more so than ‘are we ever going to create AGI, artificial general intelligence? Are we going to have sentient beings?’ There’s no path for that in the research right now and so to me it’s not even really worth talking about.”
CEO Robbie Allen recently spoke about the importance of data strategy at Adobe’s Think Tank on The Future of AI in the Enterprise.
“People hear about artificial intelligence and all this crazy stuff that it’s doing and so they think that it’s going to be this magical solution that solves all of their problems,” he said.
“In most cases, in my experience, it’s better to walk before you can run when it comes to this, especially if you’ve never done a machine learning style project before which requires a significant amount of data. You first have to to get your data strategy in place first before you can have an AI strategy.”
Later, Robbie spoke about a common data issue many enterprises may not be expecting: the high cost of getting data out of their systems.
“It’s likely that companies will spend just as much money on getting data out of their systems as they did to implement the systems to begin with,” he said.
“What I mean by that are ERPs, CRMs, all their databases. To get the data out of it in a format that’s useful for machine learning algorithms and other things is a nontrivial task.”
“And it turns out to be, in most cases, the long pole in the tent. That is, the hardest aspect of actually implementing a project is just to get the data. So that’s why we were talking about before, data strategy, when do you get it – it’s going to be difficult to do much in the space in the enterprise unless you have access to data in a format that makes it available to somebody to use in terms of implementing algorithms.”
We were proud sponsors of Duke’s first machine learning day, and excited that our Chief Scientist Larry Carin was a speaker. Of course, he’s also Duke’s Vice Provost for Research and a Professor of Electrical and Computer Engineering.
Because one ML Day just isn’t enough, we were proud to sponsor this one too. It also took place at Duke University, and our Chief Scientist Larry Carin spoke once again. Some attendees even scored some swag from our Infinia ML booth!