Summary: Recently we’ve been profiling Automated Machine Learning (AML) platforms, both of the professional variety, and particularly those proprietary one-click-to-model variety that are being pitched to untrained analysts and line-of-business managers. Since our first article, readers have suggested some additional companies we should look at which are profiled here along with some interesting observations about who is buying and why.
Added by William Vorhies on August 15, 2017 at 2:21pm — No Comments
Summary: There are a variety of new Automated Machine Learning (AML) platforms emerging that led us recently to ask if we’d be automated and unemployed any time soon. In this article we’ll cover the “Professional AML tools”. They require that you be fluent in R or Python which means that Citizen Data Scientists won’t be using them. They also significantly enhance productivity and reduce the redundant and tedious work that’s part of model…Continue
Added by William Vorhies on July 25, 2017 at 1:36pm — No Comments
Summary: A year ago we wrote about the emergence of fully automated predictive analytic platforms including some with true One-Click Data-In Model-Out capability. We revisited the five contenders from last year with one new addition and found the automation movement continues to move forward. We also observed some players from last year have now gone in different directions. …Continue
Summary: You’d think that the internet was the core of the digital economy but it’s not. Data science is the core without which the digital economy wouldn’t exist and increasingly it’s AI that’s moving the needle in consumer engagement.
Added by William Vorhies on July 11, 2017 at 6:46am — No Comments
Summary: Is everyone a ‘data scientist’? What about ‘data engineers’ and the junior versus senior, or skill level distinctions? We do seem to need some agreement about titling. Data Scientists is still the prestige title but there are some folks lobbying to take that title away.
Summary: The drive toward transparency and explainability in our modeling seems unstoppable. Up to now that meant sacrificing accuracy for interpretability. However, the ensemble method known as RuleFit may be the answer with both explainability and accuracy meeting or exceeding Random Forest.
If you’re like me and not doing modeling in a highly regulated industry like mortgage finance or insurance then when you produce a model, you are…Continue
Added by William Vorhies on June 27, 2017 at 10:02am — No Comments
Summary: There’s a three way technology race to bring faster, easier, cheaper, and smarter AI. High Performance Computing is available today but so are new commercial versions of actual Quantum computers and Neuromorphic Spiking Neural Nets. These two new entrants are going to revolutionize AI and deep learning starting now.
Summary: Quantum computing is already being used in deep learning and promises dramatic reductions in processing time and resource utilization to train even the most complex models. Here are a few things you need to know.
Added by William Vorhies on June 13, 2017 at 8:00am — No Comments
Summary: This is the second in our multi-part series on Quantum computing. In this article we’ll dive a little deeper into what’s available, what’s coming soon, and some considerations for getting in early.
In the first article in this series, “…Continue
Summary: Quantum computing is now a commercial reality. Here’s the story of the companies that are currently using it in operations and how this will soon disrupt artificial intelligence and deep learning.
Summary: This is a lesson in how it may be possible to snatch victory from the jaws of defeat. 1.) A good ROC score does not necessarily mean a good model. 2.) Even a weak model may be good at the top and bottom – consider how you can use that.
This is a lesson in how it may be possible to snatch victory from the jaws of defeat. In our world, defeat is ending up with a poor model that doesn’t do what you’d hoped. This story about a particular project…Continue
Added by William Vorhies on May 23, 2017 at 5:55am — No Comments
Summary: Someone had to say it. In my opinion R is not the best way to learn data science and not the best way to practice it either. More and more large employers agree.
Summary: Just where are we in the Age of AI, where are we going, and what happens when we get there?
When things are changing fast, sometimes it’s necessary to take a step back and see where you are. It’s very easy to get caught up in the excitement over the details. The individual data science technologies that underlie AI are all moving forward on different paths at different speeds, but all of those speeds are fast. So before you change careers or…Continue
Summary: Just how accurate are algorithms at spotting fake news and are we ready to turn them loose to suppress material they don’t find credible. Here are some considerations and stories about some of the companies trying to build these fact-checkers.Continue
Summary: What are the real threats of job loss from real and AI enhanced virtual robots? How do we position ourselves and our children to succeed in this new environment?
Added by William Vorhies on April 25, 2017 at 8:09am — No Comments
Summary: We are swept up by the rapid advances in AI and deep learning, and tend to laugh off AI’s failures as good fodder for YouTube videos. But those failures are starting to add up. It’s time to take a hard look at the weaknesses in AI and where that’s leading us.
Added by William Vorhies on April 18, 2017 at 8:04am — No Comments
Summary: The argument in the popular press about robots taking our jobs fails in the most fundamental way to differentiate between robots and AI. Here we try to identify how each contributes to job loss and what the future of AI Enhanced Robots means for employment. …Continue
Summary: DataOps is a series of principles and practices that promises to bring together the conflicting goals of the different data tribes in the organization, data science, BI, line of business, operations, and IT. What has been a growing body of best practices is now becoming the basis for a new category of data access, blending, and deployment platforms that may solve data conflicts in your organization.
Summary: Autonomous Vehicles (AVs) are supposed to be just around the corner but the anecdotal evidence is that their claims to safety are way out ahead of reality. The solution may be in a shared segment of on-board telematics, part of the SLAM group (simultaneous localization and mapping) and sharing some of that data car-to-car.
Added by William Vorhies on March 28, 2017 at 8:54am — No Comments
Summary: Some observations about new major trends and directions in data science drawn from the Strata+Hadoop conference in San Jose last week.
Added by William Vorhies on March 20, 2017 at 4:48pm — No Comments