Summary: This is the second in our “Off the Beaten Path” series looking at innovators in machine learning who have elected strategies and methods outside of the mainstream. In this article we look at Numenta’s unique approach to scalar prediction and anomaly detection based on their own brain research.
Added by William Vorhies on February 20, 2018 at 8:30am — No Comments
Summary: How about a deep learning technique based on decision trees that outperforms CNNs and RNNs, runs on your ordinary desktop, and trains with relatively small datasets. This could be a major disruptor for AI.
Summary: There are an increasing number of larger companies that have truly embraced advanced analytics and deploy fairly large numbers of data scientists. Many of these same companies are the one’s beginning to ask about using AI. Here are some observations and tips on the problems and opportunities associated with managing a larger data science function.
Summary: Exceptions sometimes make the best rules. Here’s an example of well accepted variable reduction techniques resulting in an inferior model and a case for dramatically expanding the number of variables we start with.
Summary: Advanced analytic platform developers, cloud providers, and the popular press are promoting the idea that everything we do in data science is AI. That may be good for messaging but it’s misleading to the folks who are asking us for AI solutions and makes our life all the more difficult.
Summary: Deep changes are underway in how data science is practiced and successfully deployed to solve business problems and create strategic advantage. These same changes point to major changes in how data scientists will do their work. Here’s why and how.
Summary: Where do we look to see the most advanced chatbots and the most complete application of AI? Chatbots designed as ‘artificially intelligent psychological counseling chatbots’, ‘therapeutic assistants’ for short.
Summary: Digital Twins is a concept based in IoT but requiring the skills of machine learning and potentially AI. It’s not completely new but it is integral to Gartner’s vision of the digital enterprise and makes the Hype Cycle for 2017. It’s a major enabler of event processing as opposed to traditional request processing.
Added by William Vorhies on January 2, 2018 at 8:30am — No Comments
Summary: For your holiday reading we present this selection of robot and AI fails. We hope this brings you hope and cheer for the coming year to know that our robot overlords are not as close as some think.
For your holiday reading we present this selection of robot and AI fails. We hope this brings you hope and cheer for the coming year to know that our robot overlords are not as close as some think.
Summary: If you have committed to data and analytics as strategically important to your business, should you have a Chief Data Officer (CDO) or a Chief Analytics Officer (CAO)? What’s the difference and what’s the trend?
As predictive analytics has increasingly…Continue
Added by William Vorhies on December 18, 2017 at 5:06pm — No Comments
Summary: Here are our 6 predictions for data science, machine learning, and AI for 2018. Some are fast track and potentially disruptive, some take the hype off over blown claims and set realistic expectations for the coming year.
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.
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