Summary: As a profession we do a pretty poor job of agreeing on good naming conventions for really important parts of our professional lives. “Machine Learning” is just the most recent case in point. It’s had a perfectly good definition for a very long time, but now the deep learning folks are trying to hijack the term. Come on folks. Let’s make up our minds.
As a profession we do a pretty poor job of agreeing on good naming conventions…Continue
Summary: Which is more important, the data or the algorithms? This chicken and egg question led me to realize that it’s the data, and specifically the way we store and process the data that has dominated data science over the last 10 years. And it all leads back to Hadoop.
Summary: This is the third in our series on chatbots. In this installment we’ll look at the best practice dos and don’ts as described by a number of successful chatbot developers.
Added by William Vorhies on November 20, 2017 at 9:42am — No Comments
Summary: This is the second in our chatbot series. Here we explore Natural Language Understanding (NLU), the front end of all chatbots. We’ll discuss the programming necessary to build rules based chatbots and then look at the use of deep learning algorithms that are the basis for AI enabled chatbots.
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: This is the first in a series about Chatbots. In this first installment we cover the basics including their brief technological history, uses, basic design choices, and where deep learning comes into play. In subsequent articles we’ll describe in more detail about how they are actually programmed and best practice dos and don’ts.
Summary: The addition of AI capabilities to our personal devices, applications, and even self-driving cars has caused us to take a much deeper look at what we call ‘User Experience’ (Ux). A more analytical framework identified as Cognitive Ergonomics is becoming an important field for data scientists to understand and implement.
Added by William Vorhies on October 31, 2017 at 9:51am — No Comments
Summary: We are approaching a time when we need to be concerned that our AI robots may indeed harm us. The rapid increase in the conversation about what ethics should apply to AI is appropriate but needs to be focused on the real threats, not just the wild imaginings of the popular press. Here are some data points to help you in thinking about this, what our concerns should be today, and what our concerns should be in the future.
Added by William Vorhies on October 24, 2017 at 9:26am — No Comments
Summary: Unless you’re involved in anomaly detection you may never have heard of Unsupervised Decision Trees. It’s a very interesting approach to decision trees that on the surface doesn’t sound possible but in practice is the backbone of modern intrusion detection.
I was at a presentation recently that focused on stream processing but the use case presented was about anomaly detection. When they started talking about unsupervised decision trees my…Continue
Summary: Over the last eight years predictive analytics has become a fully mature technology with wide adoption among the largest and most successful companies. The Advanced Analytic Platforms we have to make our work more effective and efficient also show substantial improvement.
Summary: If you are mid-career and thinking about switching into data science here are some things to think about in planning your journey.
We get lots of inquiries from readers asking for career advice and many of these identify as mid-career looking to switch into data science. If you’re in this group you face some of the same challenges beginners do but also some that are unique to your circumstance. Here are some thoughts and observations that may…Continue
Summary: As your data lake grows larger and your user group more diverse you will need these tools that automatically catalog data and control access to your information. They are a huge benefit and only enhance the spirit of free exploration of data for new value.
Added by William Vorhies on September 26, 2017 at 9:32am — No Comments
Summary: In just the last 10 months based only on facial characteristics deep learning has been used to predict who is a criminal and who is gay. These are rigorous, peer reviewed studies published in academic journals. How should this knowledge be used and how will the public react?
Summary: With only slight tongue in cheek about the road ahead we report on the just passed House of Representative’s new “Federal Automated Vehicle Policy” as well as similar policy just emerging in Germany. As a model of regulation on emerging AI technology we think they got this just about right.
Added by William Vorhies on September 12, 2017 at 9:35am — No Comments
Summary: Dealing with imbalanced datasets is an everyday problem. SMOTE, Synthetic Minority Oversampling TEchnique and its variants are techniques for solving this problem through oversampling that have recently become a very popular way to improve model performance.
Summary: In the first part of this series we described the basics of Reinforcement Learning (RL). In this article we describe how deep learning is augmenting RL and a variety of challenges and considerations that need to be addressed in each implementation.
Added by William Vorhies on August 29, 2017 at 9:03am — No Comments
Summary: Reinforcement Learning (RL) is likely to be the next big push in artificial intelligence. It’s the core technique for robotics, smart IoT, game play, and many other emerging areas. But the concept of modeling in RL is very different from our statistical techniques and deep learning. In this two part series we’ll take a look at the basics of RL models, how they’re built and used. In the next part, we’ll address some of the complexities that make development a…Continue
Added by William Vorhies on August 22, 2017 at 9:00am — No Comments
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.
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